Context: I have a DataFrame with 2 columns: word and vector. Where the column type of "vector" is VectorUDT.
An Example:
word | vector
assert | [435,323,324,212...]
And I want to get this:
word | v1 | v2 | v3 | v4 | v5 | v6 ......
assert | 435 | 5435| 698| 356|....
Question:
How can I split a column with vectors in several columns for each dimension using PySpark ?
Thanks in advance
Spark >= 3.0.0
Since Spark 3.0.0 this can be done without using UDF.
from pyspark.ml.functions import vector_to_array
(df
.withColumn("xs", vector_to_array("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|
## +-------+-----+-----+-----+
Spark < 3.0.0
One possible approach is to convert to and from RDD:
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|
## +-------+---+---+---+
An alternative solution would be to create an 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()
# Important: asNondeterministic requires Spark 2.3 or later
# It can be safely removed i.e.
# return udf(to_array_, ArrayType(DoubleType()))(col)
# but at the cost of decreased performance
return udf(to_array_, ArrayType(DoubleType())).asNondeterministic()(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|
## +-------+-----+-----+-----+
For Scala equivalent see Spark Scala: How to convert Dataframe[vector] to DataFrame[f1:Double, ..., fn: Double)].
To split the rawPrediction or probability columns generated after training a PySpark ML model into Pandas columns, you can split like this:
your_pandas_df['probability'].apply(lambda x: pd.Series(x.toArray()))
It is much faster to use the i_th udf from how-to-access-element-of-a-vectorudt-column-in-a-spark-dataframe
The extract function given in the solution by zero323 above uses toList, which creates a Python list object, populates it with Python float objects, finds the desired element by traversing the list, which then needs to be converted back to java double; repeated for each row. Using the rdd is much slower than the to_array udf, which also calls toList, but both are much slower than a udf that lets SparkSQL handle most of the work.
Timing code comparing rdd extract and to_array udf proposed here to i_th udf from 3955864:
from pyspark.context import SparkContext
from pyspark.sql import Row, SQLContext, SparkSession
from pyspark.sql.functions import lit, udf, col
from pyspark.sql.types import ArrayType, DoubleType
import pyspark.sql.dataframe
from pyspark.sql.functions import pandas_udf, PandasUDFType
sc = SparkContext('local[4]', 'FlatTestTime')
spark = SparkSession(sc)
spark.conf.set("spark.sql.execution.arrow.enabled", True)
from pyspark.ml.linalg import Vectors
# copy the two rows in the test dataframe a bunch of times,
# make this small enough for testing, or go for "big data" and be prepared to wait
REPS = 20000
df = sc.parallelize([
("assert", Vectors.dense([1, 2, 3]), 1, Vectors.dense([4.1, 5.1])),
("require", Vectors.sparse(3, {1: 2}), 2, Vectors.dense([6.2, 7.2])),
] * REPS).toDF(["word", "vector", "more", "vorpal"])
def extract(row):
return (row.word, ) + tuple(row.vector.toArray().tolist(),) + (row.more,) + tuple(row.vorpal.toArray().tolist(),)
def test_extract():
return df.rdd.map(extract).toDF(['word', 'vector__0', 'vector__1', 'vector__2', 'more', 'vorpal__0', 'vorpal__1'])
def to_array(col):
def to_array_(v):
return v.toArray().tolist()
return udf(to_array_, ArrayType(DoubleType()))(col)
def test_to_array():
df_to_array = df.withColumn("xs", to_array(col("vector"))) \
.select(["word"] + [col("xs")[i] for i in range(3)] + ["more", "vorpal"]) \
.withColumn("xx", to_array(col("vorpal"))) \
.select(["word"] + ["xs[{}]".format(i) for i in range(3)] + ["more"] + [col("xx")[i] for i in range(2)])
return df_to_array
# pack up to_array into a tidy function
def flatten(df, vector, vlen):
fieldNames = df.schema.fieldNames()
if vector in fieldNames:
names = []
for fieldname in fieldNames:
if fieldname == vector:
names.extend([col(vector)[i] for i in range(vlen)])
else:
names.append(col(fieldname))
return df.withColumn(vector, to_array(col(vector)))\
.select(names)
else:
return df
def test_flatten():
dflat = flatten(df, "vector", 3)
dflat2 = flatten(dflat, "vorpal", 2)
return dflat2
def ith_(v, i):
try:
return float(v[i])
except ValueError:
return None
ith = udf(ith_, DoubleType())
select = ["word"]
select.extend([ith("vector", lit(i)) for i in range(3)])
select.append("more")
select.extend([ith("vorpal", lit(i)) for i in range(2)])
# %% timeit ...
def test_ith():
return df.select(select)
if __name__ == '__main__':
import timeit
# make sure these work as intended
test_ith().show(4)
test_flatten().show(4)
test_to_array().show(4)
test_extract().show(4)
print("i_th\t\t",
timeit.timeit("test_ith()",
setup="from __main__ import test_ith",
number=7)
)
print("flatten\t\t",
timeit.timeit("test_flatten()",
setup="from __main__ import test_flatten",
number=7)
)
print("to_array\t",
timeit.timeit("test_to_array()",
setup="from __main__ import test_to_array",
number=7)
)
print("extract\t\t",
timeit.timeit("test_extract()",
setup="from __main__ import test_extract",
number=7)
)
Results:
i_th 0.05964796099999958
flatten 0.4842299350000001
to_array 0.42978780299999997
extract 2.9254476840000017
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)
The function turns the feature vector column into separate columns
