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From MechCoder <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-6485] [MLlib] [Python] Add CoordinateMa...
Date Wed, 22 Jul 2015 18:07:53 GMT
Github user MechCoder commented on a diff in the pull request:

    https://github.com/apache/spark/pull/7554#discussion_r35244034
  
    --- Diff: python/pyspark/mllib/linalg.py ---
    @@ -1152,9 +1156,385 @@ def sparse(numRows, numCols, colPtrs, rowIndices, values):
             return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values)
     
     
    +class DistributedMatrix(object):
    +    """Represents a distributively stored matrix backed by one or more RDDs."""
    +    def numRows(self):
    +        """Get or compute the number of rows."""
    +        raise NotImplementedError
    +
    +    def numCols(self):
    +        """Get or compute the number of cols."""
    +        raise NotImplementedError
    +
    +
    +class DistributedMatrices(object):
    +    """Factory methods for distributed matrices."""
    +    @staticmethod
    +    def rowMatrix(rows, numRows=0, numCols=0):
    +        """
    +        Create a RowMatrix.
    +
    +        :param rows: An RDD of Vectors.
    +        """
    +        javaRowMatrix = callMLlibFunc("createRowMatrix", rows, long(numRows), int(numCols))
    +        jrm = JavaModelWrapper(javaRowMatrix)
    +        return RowMatrix(jrm)
    +
    +    @staticmethod
    +    def indexedRowMatrix(rows, numRows=0, numCols=0):
    +        """
    +        Create an IndexedRowMatrix.
    +
    +        :param rows: An RDD of IndexedRows or (long, Vector) tuples.
    +        """
    +        # We use DataFrames for serialization of IndexedRows from Python, so convert
the RDD to a
    +        # DataFrame. This will convert each IndexedRow to a Row containing the 'index'
and 'vector'
    +        # values, which can both be easily serialized.  We will convert back to IndexedRows
on the
    +        # Scala side.
    +        javaIndexedRowMatrix = callMLlibFunc("createIndexedRowMatrix", rows.toDF(),
    +                                             long(numRows), int(numCols))
    +        jirm = JavaModelWrapper(javaIndexedRowMatrix)
    +        return IndexedRowMatrix(jirm)
    +
    +    @staticmethod
    +    def coordinateMatrix(entries, numRows=0, numCols=0):
    +        """
    +        Create a CoordinateMatrix.
    +
    +        :param entries: An RDD of MatrixEntry inputs or (long, long, float) tuples.
    +        """
    +        # We use DataFrames for serialization of MatrixEntry inputs from Python, so convert
the RDD
    +        # to a DataFrame. This will convert each MatrixEntry to a Row containing the
'i', 'j', and
    +        # 'value' values, which can each be easily serialized.  We will convert back
to MatrixEntry
    +        # inputs on the Scala side.
    +        javaCoordinateMatrix = callMLlibFunc("createCoordinateMatrix", entries.toDF(),
    +                                             long(numRows), long(numCols))
    +        jcm = JavaModelWrapper(javaCoordinateMatrix)
    +        return CoordinateMatrix(jcm)
    +
    +
    +class RowMatrix(DistributedMatrix):
    +    """
    +    Represents a row-oriented distributed Matrix with no meaningful row indices.
    +
    +    .. note:: Experimental
    +    """
    +    def __init__(self, jrm):
    +        """ Create a wrapper over a Java RowMatrix. """
    +        self._jrm = jrm
    +        self.rows = self._rows()
    +
    +    def _rows(self):
    +        """
    +        Get the rows of the RowMatrix as a RDD of Vectors.
    +
    +        >>> rows = sc.parallelize([Vectors.dense([1, 2, 3]), Vectors.dense([4,
5, 6]),
    --- End diff --
    
    There are some rules in PEP8 like if we limit the line length to 100 in code, we need
to limit the docstring length to 72. I don't understand why, but since they are rules it might
be better to follow them :) (http://legacy.python.org/dev/peps/pep-0008/#maximum-line-length)


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