I need to delete accents from characters in Spanish and others languages from different datasets.
I already did a function based in the code provided in this post that removes special the accents. The problem is that the function is slow because it uses an UDF.
I'm just wondering if I can improve the performance of my function to get results in less time, because this is good for small dataframes but not for big ones.
Thanks in advance.
Here the code, you will be able to run it as it is presented:
# Importing sql types
from pyspark.sql.types import StringType, IntegerType, StructType, StructField
from pyspark.sql.functions import udf, col
import unicodedata
# Building a simple dataframe:
schema = StructType([StructField("city", StringType(), True),
StructField("country", StringType(), True),
StructField("population", IntegerType(), True)])
countries = ['Venezuela', 'US@A', 'Brazil', 'Spain']
cities = ['Maracaibó', 'New York', ' São Paulo ', '~Madrid']
population = [37800000,19795791,12341418,6489162]
# Dataframe:
df = sqlContext.createDataFrame(list(zip(cities, countries, population)), schema=schema)
df.show()
class Test():
def __init__(self, df):
self.df = df
def clearAccents(self, columns):
"""This function deletes accents in strings column dataFrames,
it does not eliminate main characters, but only deletes special tildes.
:param columns String or a list of column names.
"""
# Filters all string columns in dataFrame
validCols = [c for (c, t) in filter(lambda t: t[1] == 'string', self.df.dtypes)]
# If None or [] is provided with column parameter:
if (columns == "*"): columns = validCols[:]
# Receives a string as an argument
def remove_accents(inputStr):
# first, normalize strings:
nfkdStr = unicodedata.normalize('NFKD', inputStr)
# Keep chars that has no other char combined (i.e. accents chars)
withOutAccents = u"".join([c for c in nfkdStr if not unicodedata.combining(c)])
return withOutAccents
function = udf(lambda x: remove_accents(x) if x != None else x, StringType())
exprs = [function(col(c)).alias(c) if (c in columns) and (c in validCols) else c for c in self.df.columns]
self.df = self.df.select(*exprs)
foo = Test(df)
foo.clearAccents(columns="*")
foo.df.show()
One possible improvement is to build a custom Transformer, which will handle Unicode normalization, and corresponding Python wrapper. It should reduce overall overhead of passing data between JVM and Python and doesn't require any modifications in Spark itself or access to private API.
On JVM side you'll need a transformer similar to this one:
package net.zero323.spark.ml.feature
import java.text.Normalizer
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{DataType, StringType}
class UnicodeNormalizer (override val uid: String)
extends UnaryTransformer[String, String, UnicodeNormalizer] {
def this() = this(Identifiable.randomUID("unicode_normalizer"))
private val forms = Map(
"NFC" -> Normalizer.Form.NFC, "NFD" -> Normalizer.Form.NFD,
"NFKC" -> Normalizer.Form.NFKC, "NFKD" -> Normalizer.Form.NFKD
)
val form: Param[String] = new Param(this, "form", "unicode form (one of NFC, NFD, NFKC, NFKD)",
ParamValidators.inArray(forms.keys.toArray))
def setN(value: String): this.type = set(form, value)
def getForm: String = $(form)
setDefault(form -> "NFKD")
override protected def createTransformFunc: String => String = {
val normalizerForm = forms($(form))
(s: String) => Normalizer.normalize(s, normalizerForm)
}
override protected def validateInputType(inputType: DataType): Unit = {
require(inputType == StringType, s"Input type must be string type but got $inputType.")
}
override protected def outputDataType: DataType = StringType
}
Corresponding build definition (adjust Spark and Scala versions to match your Spark deployment):
name := "unicode-normalization"
version := "1.0"
crossScalaVersions := Seq("2.11.12", "2.12.8")
organization := "net.zero323"
val sparkVersion = "2.4.0"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % sparkVersion,
"org.apache.spark" %% "spark-sql" % sparkVersion,
"org.apache.spark" %% "spark-mllib" % sparkVersion
)
On Python side you'll need a wrapper similar to this one.
from pyspark.ml.param.shared import *
# from pyspark.ml.util import keyword_only # in Spark < 2.0
from pyspark import keyword_only
from pyspark.ml.wrapper import JavaTransformer
class UnicodeNormalizer(JavaTransformer, HasInputCol, HasOutputCol):
@keyword_only
def __init__(self, form="NFKD", inputCol=None, outputCol=None):
super(UnicodeNormalizer, self).__init__()
self._java_obj = self._new_java_obj(
"net.zero323.spark.ml.feature.UnicodeNormalizer", self.uid)
self.form = Param(self, "form",
"unicode form (one of NFC, NFD, NFKC, NFKD)")
# kwargs = self.__init__._input_kwargs # in Spark < 2.0
kwargs = self._input_kwargs
self.setParams(**kwargs)
@keyword_only
def setParams(self, form="NFKD", inputCol=None, outputCol=None):
# kwargs = self.setParams._input_kwargs # in Spark < 2.0
kwargs = self._input_kwargs
return self._set(**kwargs)
def setForm(self, value):
return self._set(form=value)
def getForm(self):
return self.getOrDefault(self.form)
Build Scala package:
sbt +package
include it when you start shell or submit. For example for Spark build with Scala 2.11:
bin/pyspark --jars path-to/target/scala-2.11/unicode-normalization_2.11-1.0.jar \
--driver-class-path path-to/target/scala-2.11/unicode-normalization_2.11-1.0.jar
and you should be ready to go. All what is left is a little bit of regexp magic:
from pyspark.sql.functions import regexp_replace
normalizer = UnicodeNormalizer(form="NFKD",
inputCol="text", outputCol="text_normalized")
df = sc.parallelize([
(1, "Maracaibó"), (2, "New York"),
(3, " São Paulo "), (4, "~Madrid")
]).toDF(["id", "text"])
(normalizer
.transform(df)
.select(regexp_replace("text_normalized", "\p{M}", ""))
.show())
## +--------------------------------------+
## |regexp_replace(text_normalized,\p{M},)|
## +--------------------------------------+
## | Maracaibo|
## | New York|
## | Sao Paulo |
## | ~Madrid|
## +--------------------------------------+
Please note that this follows the same conventions as built in text transformers and is not null safe. You can easily correct for that by check for null in createTransformFunc.