我有一个Python函数:
def get_log_probability(string, transition_log_probabilities):
string = ngrams(string, 2)
terms = [transition_log_probabilities[bigram]
for bigram in string]
log_probability = sum(terms)/len(terms) if len(terms) > 0 else sum(terms)
return log_probability
我想将此函数用于transition_log_probabilities
为常量的Pyspark DataFrame列,如下所示:
transition_log_probabilities = {('a', 'a'): -3.688879454113936,
('a', 'b'): -3.688879454113936,
('a', 'c'): -3.688879454113936,
('b', 'a'): -3.688879454113936,
('b', 'b'): -3.688879454113936,
('b', 'c'): -3.688879454113936,
('c', 'a'): -3.688879454113936,
('c', 'b'): -3.688879454113936,
('c', 'c'): -3.688879454113936}
所以我把它改成Pyspark UDF:
def get_log_prob_udf(dictionary):
return udf(lambda string: get_log_probability(string, dictionary), FloatType())
即使get_log_probability("abc", transition_log_probabilities)
起作用并给出-3.688879454113936
的结果,当我将其UDF应用于Pyspark时,如下所示:
df = df
.withColumn("string_log_probability", get_log_prob_udf(transition_log_probabilities)(col('string')))
它不起作用,并抛出的错误
An error occurred while calling o3463.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage
182.0 failed 1 times, most recent failure: Lost task 0.0 in stage 182.0 (TID 774)
(kubernetes.docker.internal executor driver): net.razorvine.pickle.PickleException:
expected zero arguments for construction of ClassDict (for numpy.dtype)
有人知道怎么解决吗?非常感谢。
希望这就是你想要的结果。
df = spark.createDataFrame( [ (1, "bc"), (2, "aa"), (3, "ca") ], ["id", "string"] )
from pyspark.sql import functions as F, types as T
from nltk import ngrams
transition_log_probabilities = {('a', 'a'): -3.688879454113936,
('a', 'b'): -3.688879454113936,
('a', 'c'): -3.688879454113936,
('b', 'a'): -3.688879454113936,
('b', 'b'): -3.688879454113936,
('b', 'c'): -3.688879454113936,
('c', 'a'): -3.688879454113936,
('c', 'b'): -3.688879454113936,
('c', 'c'): -3.688879454113936}
def get_log_probability(string):
string = ngrams(string, 2)
terms = [transition_log_probabilities[bigram]
for bigram in string]
log_probability = sum(terms)/len(terms) if len(terms) > 0 else sum(terms)
return log_probability
get_log_prob_udf = udf(get_log_probability, T.FloatType())
df.withColumn('string_log_probability', get_log_prob_udf(F.col('string'))).show()
+---+------+----------------------+
| id|string|string_log_probability|
+---+------+----------------------+
| 1| bc| -3.6888795|
| 2| aa| -3.6888795|
| 3| ca| -3.6888795|
+---+------+----------------------+