'JavaPackage' 对象不可调用 - MLeap

Question

当我尝试使用以下代码使用 MLeap序列化模型时：

import mleap.pyspark
from mleap.pyspark.spark_support import SimpleSparkSerializer

# Import standard PySpark Transformers and packages
from pyspark.ml.feature import VectorAssembler, StandardScaler, OneHotEncoder, StringIndexer
from pyspark.ml import Pipeline, PipelineModel
from pyspark.sql import Row

# Create a test data frame
l = [('Alice', 1), ('Bob', 2)]
rdd = sc.parallelize(l)
Person = Row('name', 'age')
person = rdd.map(lambda r: Person(*r))
df2 = spark.createDataFrame(person)
df2.collect()

# Build a very simple pipeline using two transformers
string_indexer = StringIndexer(inputCol='name', outputCol='name_string_index')

feature_assembler = VectorAssembler(inputCols=[string_indexer.getOutputCol()], outputCol="features")

feature_pipeline = [string_indexer, feature_assembler]

featurePipeline = Pipeline(stages=feature_pipeline)

fittedPipeline = featurePipeline.fit(df2)


# serialize the model:
fittedPipeline.serializeToBundle("jar:file:/tmp/pyspark.example.zip", fittedPipeline.transform(df2))

但是我收到以下错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-98a49e4cd023> in <module>()
----> 1 fittedPipeline.serializeToBundle("jar:file:/tmp/pyspark.example.zip", fittedPipeline.transform(df2))

/opt/anaconda2/envs/py345/lib/python3.4/site-packages/mleap/pyspark/spark_support.py in serializeToBundle(self, path, dataset)
     22 
     23 def serializeToBundle(self, path, dataset=None):
---> 24     serializer = SimpleSparkSerializer()
     25     serializer.serializeToBundle(self, path, dataset=dataset)
     26 

/opt/anaconda2/envs/py345/lib/python3.4/site-packages/mleap/pyspark/spark_support.py in __init__(self)
     37     def __init__(self):
     38         super(SimpleSparkSerializer, self).__init__()
---> 39         self._java_obj = _jvm().ml.combust.mleap.spark.SimpleSparkSerializer()
     40 
     41     def serializeToBundle(self, transformer, path, dataset):

TypeError: 'JavaPackage' object is not callable

请帮忙？

Answer 1

我通过下载并指向 spark 提交脚本中丢失的 jar 文件设法解决了这个问题。就我而言，我安装了 MLeap 0.8.1 并使用基于 Scalar11 构建的 Spark2，因此我从 MvnRepository 下载了以下 jar 文件：

metrics-core-2.2.0
mleap-base_2.11-0.8.1
mleap-core_2.11-0.8.1
mleap-runtime_2.11-0.8.1
mleap-spark_2.11-0.8.1
mleap-spark-base_2.11-0.8.1
mleap-tensor_2.11-0.8.1

然后我还使用我的 spark 提交文件上的 --jar 标志指向这个 jar 文件，如下所示（我还使用 --repository 标志指向 maven 存储库）：

export PYSPARK_SUBMIT_ARGS='--master yarn --deploy-mode client --driver-memory 40g --num-executors 15 --executor-memory 30g --executor-cores 5 --packages ml.combust.mleap:mleap-runtime_2.11:0.8.1 --repositories http://YOUR MAVEN REPO/ --jars arpack_combined_all-0.1.jar,mleap-base_2.11-0.8.1.jar,mleap-core_2.11-0.8.1.jar,mleap-runtime_2.11-0.8.1.jar,mleap-spark_2.11-0.8.1.jar,mleap-spark-base_2.11-0.8.1.jar,mleap-tensor_2.11-0.8.1.jar pyspark-shell'
jupyter notebook --no-browser --ip=$(hostname -f)

-Source

Answer 2

@Tshilidzi Madau 的回答是正确的——您需要做的是将 mleap-spark jar 添加到您的 spark 类路径中。

pyspark 中的一个选项是在创建 SparkSession 时设置 spark.jars.packages 配置：

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .config('spark.jars.packages', 'ml.combust.mleap:mleap-spark_2.12:0.19.0') \
    .config("spark.jars.excludes", "net.sourceforge.f2j:arpack_combined_all") \ # this exclude is needed as this lib seems not to be available in public maven repos
    .getOrCreate()

我用 Spark 3.0.3 和 mleap 0.19.0

测试了它

'JavaPackage' 对象不可调用 - MLeap

'JavaPackage' object is not callable - MLeap

pyspark

mleap