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  1. Spark
  2. SPARK-20214

pyspark linalg _convert_to_vector should check for sorted indices

    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 1.5.2, 1.6.3, 2.0.2, 2.1.1, 2.2.0
    • Fix Version/s: 2.0.3, 2.1.1, 2.2.0
    • Component/s: ML, MLlib, PySpark, Tests
    • Labels:
      None

      Description

      I've seen a few failures of this line: https://github.com/apache/spark/blame/402bf2a50ddd4039ff9f376b641bd18fffa54171/python/pyspark/mllib/tests.py#L847

      It converts a scipy.sparse.lil_matrix to a dok_matrix and then to a pyspark.mllib.linalg.Vector. The failure happens in the conversion to a vector and indicates that the dok_matrix is not returning its values in sorted order. (Actually, the failure is in _convert_to_vector, which converts the dok_matrix to a csc_matrix and then passes the CSC data to the MLlib Vector constructor.) Here's the stack trace:

      Traceback (most recent call last):
        File "/home/jenkins/workspace/python/pyspark/mllib/tests.py", line 847, in test_serialize
          self.assertEqual(sv, _convert_to_vector(lil.todok()))
        File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 78, in _convert_to_vector
          return SparseVector(l.shape[0], csc.indices, csc.data)
        File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 556, in __init__
          % (self.indices[i], self.indices[i + 1]))
      TypeError: Indices 3 and 1 are not strictly increasing
      

      This seems like a bug in _convert_to_vector, where we really should check csc_matrix.has_sorted_indices first.

      I haven't seen this bug in pyspark.ml.linalg, but it probably exists there too.

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            • Assignee:
              viirya Liang-Chi Hsieh
              Reporter:
              josephkb Joseph K. Bradley
              Shepherd:
              Joseph K. Bradley
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              • Created:
                Updated:
                Resolved: