[GitHub] spark pull request: [SPARK-9525] [PySpark] [MLlib] Optimize Sparse...

[jkbradley <gi...@git.apache.org>]

Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/7854#discussion_r39117130
  
    --- Diff: python/pyspark/mllib/linalg/__init__.py ---
    @@ -461,32 +461,41 @@ def __init__(self, size, *args):
             self.size = int(size)
             """ Size of the vector. """
             assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
    -        if len(args) == 1:
    -            pairs = args[0]
    -            if type(pairs) == dict:
    -                pairs = pairs.items()
    -            pairs = sorted(pairs)
    -            self.indices = np.array([p[0] for p in pairs], dtype=np.int32)
    -            """ A list of indices corresponding to active entries. """
    -            self.values = np.array([p[1] for p in pairs], dtype=np.float64)
    -            """ A list of values corresponding to active entries. """
    +        if isinstance(args[0], bytes):
    +            assert isinstance(args[1], bytes), "values should be string too"
    +            if args[0]:
    +                self.indices = np.frombuffer(args[0], np.int32)
    +                self.values = np.frombuffer(args[1], np.float64)
    +            else:
    +                # np.frombuffer() doesn't work well with empty string in older version
    +                self.indices = np.array([], dtype=np.int32)
    +                self.values = np.array([], dtype=np.float64)
             else:
    -            if isinstance(args[0], bytes):
    -                assert isinstance(args[1], bytes), "values should be string too"
    -                if args[0]:
    -                    self.indices = np.frombuffer(args[0], np.int32)
    -                    self.values = np.frombuffer(args[1], np.float64)
    -                else:
    -                    # np.frombuffer() doesn't work well with empty string in older version
    -                    self.indices = np.array([], dtype=np.int32)
    -                    self.values = np.array([], dtype=np.float64)
    +            if len(args) == 1:
    +                args = args[0]
    +                if isinstance(args, dict):
    +                    args = args.items()
    +                args = list(zip(*args))
    +
    +            # Handle empty args case.
    +            if len(args) == 0:
    +                indices = []
    +                values = []
                 else:
    -                self.indices = np.array(args[0], dtype=np.int32)
    -                self.values = np.array(args[1], dtype=np.float64)
    -            assert len(self.indices) == len(self.values), "index and value arrays not same length"
    -            for i in xrange(len(self.indices) - 1):
    -                if self.indices[i] >= self.indices[i + 1]:
    -                    raise TypeError("indices array must be sorted")
    --- End diff --
    
    To match Scala, we should:
    * have constant-time initialization for SparseVector (for fast conversions between MLlib and Breeze/scipy)
    * allow sorting for construction via Vectors.sparse (but that can depend upon the argument types, as in Scala)


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