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?