在Pyspark中将稀疏向量转换为密集向量

Question

我有一个稀疏向量，如下所示

>>> countVectors.rdd.map(lambda vector: vector[1]).collect()
[SparseVector(13, {0: 1.0, 2: 1.0, 3: 1.0, 6: 1.0, 8: 1.0, 9: 1.0, 10: 1.0, 12: 1.0}), SparseVector(13, {0: 1.0, 1: 1.0, 2: 1.0, 4: 1.0}), SparseVector(13, {0: 1.0, 1: 1.0, 3: 1.0, 4: 1.0, 7: 1.0}), SparseVector(13, {1: 1.0, 2: 1.0, 5: 1.0, 11: 1.0})]

我尝试在pyspark 2.0.0中将其转换为密集向量，如下所示

>>> frequencyVectors = countVectors.rdd.map(lambda vector: vector[1])
>>> frequencyVectors.map(lambda vector: Vectors.dense(vector)).collect()

我得到一个错误，如下所示：

16/12/26 14:03:35 ERROR Executor: Exception in task 0.0 in stage 13.0 (TID 13)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/opt/BIG-DATA/spark-2.0.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 172, in main
    process()
  File "/opt/BIG-DATA/spark-2.0.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 167, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/opt/BIG-DATA/spark-2.0.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "<stdin>", line 1, in <lambda>
  File "/opt/BIG-DATA/spark-2.0.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/mllib/linalg/__init__.py", line 878, in dense
    return DenseVector(elements)
  File "/opt/BIG-DATA/spark-2.0.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/mllib/linalg/__init__.py", line 286, in __init__
    ar = np.array(ar, dtype=np.float64)
  File "/opt/BIG-DATA/spark-2.0.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/ml/linalg/__init__.py", line 701, in __getitem__
    raise ValueError("Index %d out of bounds." % index)
ValueError: Index 13 out of bounds.

如何实现此转换？这里有什么问题吗？