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PySpark - Liste de passes en tant que paramètre pour UDF

Je dois passer une liste dans un fichier UDF, la liste déterminera le score/la catégorie de la distance. Pour le moment, je suis difficile à coder toutes les distances pour être le 4ème score.

a= spark.createDataFrame([("A", 20), ("B", 30), ("D", 80)],["Letter", "distances"])

from pyspark.sql.functions import udf
def cate(label, feature_list):
    if feature_list == 0:
        return label[4]
label_list = ["Great", "Good", "OK", "Please Move", "Dead"]
udf_score=udf(cate, StringType())
a.withColumn("category", udf_score(label_list,a["distances"])).show(10)

quand j'essaie quelque chose comme ça, j'obtiens cette erreur. 

Py4JError: An error occurred while calling z:org.Apache.spark.sql.functions.col. Trace:
py4j.Py4JException: Method col([class Java.util.ArrayList]) does not exist
    at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.Java:318)
    at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.Java:339)
    at py4j.Gateway.invoke(Gateway.Java:274)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.Java:132)
    at py4j.commands.CallCommand.execute(CallCommand.Java:79)
    at py4j.GatewayConnection.run(GatewayConnection.Java:214)
    at Java.lang.Thread.run(Thread.Java:745)
pythonpysparkspark-dataframeuser-defined-functions
5
Bryce Ramgovind

J'espère que cela t'aides!

from pyspark.sql.functions import udf, col

#sample data
a= sqlContext.createDataFrame([("A", 20), ("B", 30), ("D", 80)],["Letter", "distances"])
label_list = ["Great", "Good", "OK", "Please Move", "Dead"]

def cate(label, feature_list):
    if feature_list == 0:
        return label[4]
    else:  #you may need to add 'else' condition as well otherwise 'null' will be added in this case
        return 'I am not sure!'

def udf_score(label_list):
    return udf(lambda l: cate(l, label_list))
a.withColumn("category", udf_score(label_list)(col("distances"))).show()

La sortie est:

+------+---------+--------------+
|Letter|distances|      category|
+------+---------+--------------+
|     A|       20|I am not sure!|
|     B|       30|I am not sure!|
|     D|       80|I am not sure!|
+------+---------+--------------+
14
Prem

Essayez de curry la fonction, de sorte que le seul argument de l'appel DataFrame soit le nom de la colonne sur laquelle vous voulez que la fonction agisse:

udf_score=udf(lambda x: cate(label_list,x), StringType())
a.withColumn("category", udf_score("distances")).show(10)
2
ags29

Je pense que cela peut aider en passant list comme valeur par défaut d'une variable

from pyspark.sql.functions import udf, col

#sample data
a= sqlContext.createDataFrame([("A", 20), ("B", 30), ("D", 80),("E",0)],["Letter", "distances"])
label_list = ["Great", "Good", "OK", "Please Move", "Dead"]

#Passing List as Default value to a variable
def cate( feature_list,label=label_list):
    if feature_list == 0:
        return label[4]
    else:  #you may need to add 'else' condition as well otherwise 'null' will be added in this case
        return 'I am not sure!'

udfcate = udf(cate, StringType())

a.withColumn("category", udfcate("distances")).show()

Sortie:

+------+---------+--------------+
|Letter|distances|      category|
+------+---------+--------------+
|     A|       20|I am not sure!|
|     B|       30|I am not sure!|
|     D|       80|I am not sure!|
|     E|        0|          Dead|
+------+---------+--------------+
0
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