I have a sparse vector like this
>>> 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})]
I am trying to convert this into dense vector in pyspark 2.0.0 like this
>>> frequencyVectors = countVectors.rdd.map(lambda vector: vector[1])
>>> frequencyVectors.map(lambda vector: Vectors.dense(vector)).collect()
I am getting an error like this:
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.
How can I achieve this conversion? Is there anything wrong here?
This resolved my issue
frequencyDenseVectors = frequencyVectors.map(lambda vector: DenseVector(vector.toArray()))