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Posted to issues@spark.apache.org by "Ziqun Ye (Jira)" <ji...@apache.org> on 2021/11/22 05:14:00 UTC
[jira] [Created] (SPARK-37435) Did not find value which can be converted into java.lang.String
Ziqun Ye created SPARK-37435:
--------------------------------
Summary: Did not find value which can be converted into java.lang.String
Key: SPARK-37435
URL: https://issues.apache.org/jira/browse/SPARK-37435
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 3.0.2, 2.4.4
Reporter: Ziqun Ye
Got this following error when loading the saved model.
```
ERROR:ADS Exception Traceback (most recent call last): File "/home/datascience/conda/pyspark30_p37_cpu_v2/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "/tmp/ipykernel_12307/1140552986.py", line 15, in <module> LogisticRegressionModel.load(spark, "./lrmodelv2") File "/home/datascience/conda/pyspark30_p37_cpu_v2/lib/python3.7/site-packages/pyspark/mllib/classification.py", line 249, in load sc._jsc.sc(), path) File "/home/datascience/conda/pyspark30_p37_cpu_v2/lib/python3.7/site-packages/py4j/java_gateway.py", line 1305, in __call__ answer, self.gateway_client, self.target_id, self.name) File "/home/datascience/conda/pyspark30_p37_cpu_v2/lib/python3.7/site-packages/pyspark/sql/utils.py", line 128, in deco return f(*a, **kw) File "/home/datascience/conda/pyspark30_p37_cpu_v2/lib/python3.7/site-packages/py4j/protocol.py", line 328, in get_return_value format(target_id, ".", name), value) py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.classification.LogisticRegressionModel.load. : org.json4s.package$MappingException: Did not find value which can be converted into java.lang.String at org.json4s.reflect.package$.fail(package.scala:95) at org.json4s.Extraction$.$anonfun$convert$2(Extraction.scala:756) at scala.Option.getOrElse(Option.scala:189) at org.json4s.Extraction$.convert(Extraction.scala:756) at org.json4s.Extraction$.$anonfun$extract$10(Extraction.scala:404) at org.json4s.Extraction$.$anonfun$customOrElse$1(Extraction.scala:658) at scala.PartialFunction.applyOrElse(PartialFunction.scala:127) at scala.PartialFunction.applyOrElse$(PartialFunction.scala:126) at scala.PartialFunction$$anon$1.applyOrElse(PartialFunction.scala:257) at org.json4s.Extraction$.customOrElse(Extraction.scala:658) at org.json4s.Extraction$.extract(Extraction.scala:402) at org.json4s.Extraction$.extract(Extraction.scala:40) at org.json4s.ExtractableJsonAstNode.extract(ExtractableJsonAstNode.scala:21) at org.apache.spark.mllib.util.Loader$.loadMetadata(modelSaveLoad.scala:122) at org.apache.spark.mllib.classification.LogisticRegressionModel$.load(LogisticRegression.scala:176) at org.apache.spark.mllib.classification.LogisticRegressionModel.load(LogisticRegression.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.classification.LogisticRegressionModel.load. : org.json4s.package$MappingException: Did not find value which can be converted into java.lang.String at org.json4s.reflect.package$.fail(package.scala:95) at org.json4s.Extraction$.$anonfun$convert$2(Extraction.scala:756) at scala.Option.getOrElse(Option.scala:189) at org.json4s.Extraction$.convert(Extraction.scala:756) at org.json4s.Extraction$.$anonfun$extract$10(Extraction.scala:404) at org.json4s.Extraction$.$anonfun$customOrElse$1(Extraction.scala:658) at scala.PartialFunction.applyOrElse(PartialFunction.scala:127) at scala.PartialFunction.applyOrElse$(PartialFunction.scala:126) at scala.PartialFunction$$anon$1.applyOrElse(PartialFunction.scala:257) at org.json4s.Extraction$.customOrElse(Extraction.scala:658) at org.json4s.Extraction$.extract(Extraction.scala:402) at org.json4s.Extraction$.extract(Extraction.scala:40) at org.json4s.ExtractableJsonAstNode.extract(ExtractableJsonAstNode.scala:21) at org.apache.spark.mllib.util.Loader$.loadMetadata(modelSaveLoad.scala:122) at org.apache.spark.mllib.classification.LogisticRegressionModel$.load(LogisticRegression.scala:176) at org.apache.spark.mllib.classification.LogisticRegressionModel.load(LogisticRegression.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748)
```
Sample code snippet
```
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
from sklearn.datasets import load_iris
from pyspark.mllib.classification import LogisticRegressionModel
spark = SparkSession.builder.getOrCreate()
df = load_iris(as_frame=True).frame.rename(columns=\{"target": "label"})
df = spark.createDataFrame(df)
df = VectorAssembler(inputCols=df.columns[:-1], outputCol="features").transform(df)
train, test = df.randomSplit([0.8, 0.2])
lor = LogisticRegression(maxIter=5)
lorModel = lor.fit(train)
lorModel.write().overwrite().save("./lrmodelv2")
LogisticRegressionModel.load(spark, "./lrmodelv2")
```
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