Andrey Vykhodtsev created SPARK-9277:
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Summary: SparseVector constructor must throw an error when
declared number of elements less than array lenght
Key: SPARK-9277
URL: https://issues.apache.org/jira/browse/SPARK-9277
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.3.1
Reporter: Andrey Vykhodtsev
Priority: Minor
I found that one can create SparseVector inconsistently and it will lead to an
Java error in runtime, for example when training LogisticRegressionWithSGD.
Here is the test case:
In [2]:
sc.version
Out[2]:
u'1.3.1'
In [13]:
from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import LogisticRegressionWithSGD
In [3]:
x = SparseVector(2, {1:1, 2:2, 3:3, 4:4, 5:5})
In [10]:
l = LabeledPoint(0, x)
In [12]:
r = sc.parallelize([l])
In [14]:
m = LogisticRegressionWithSGD.train(r)
Error:
Py4JJavaError: An error occurred while calling
o86.trainLogisticRegressionModelWithSGD.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in
stage 11.0 failed 1 times, most recent failure: Lost task 7.0 in stage 11.0
(TID 47, localhost): java.lang.ArrayIndexOutOfBoundsException: 2
Attached is the notebook with the scenario and the full message
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