Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/7554#discussion_r35572067
--- 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 RowMatrix(DistributedMatrix):
+ """
+ Represents a row-oriented distributed Matrix with no meaningful
+ row indices.
+
+ .. note:: Experimental
+ """
+ def __init__(self, rows, numRows=0, numCols=0):
+ """
+ Create a wrapper over a Java RowMatrix.
+
+ :param rows: An RDD of Vectors.
+ """
+ if not isinstance(rows, RDD):
+ raise TypeError("rows should be an RDD object, got %s" %
type(rows))
+ first = rows.first()
+ if not isinstance(first, Vector):
+ raise TypeError("rows should be an RDD of Vectors, got an RDD
of %s" % type(first))
+
+ javaRowMatrix = callMLlibFunc("createRowMatrix", rows,
long(numRows), int(numCols))
+ self._jrm = JavaModelWrapper(javaRowMatrix)
+ self.rows = rows
+
+ @staticmethod
+ def _from_java(javaRowMatrix):
+ """Create a PySpark RowMatrix from a Java RowMatrix object."""
+ rows = JavaModelWrapper(javaRowMatrix).call("rows")
+ return RowMatrix(rows)
+
+ def numRows(self):
+ """
+ Get or compute the number of rows.
+
+ >>> rows = sc.parallelize([Vectors.dense([1, 2, 3]),
Vectors.dense([4, 5, 6]),
+ ... Vectors.dense([7, 8, 9]),
Vectors.dense([10, 11, 12])])
+ >>> rm = RowMatrix(rows)
+ >>> int(rm.numRows())
+ 4
+
+ >>> rows = sc.parallelize([Vectors.dense([1, 2, 3]),
Vectors.dense([4, 5, 6]),
+ ... Vectors.dense([7, 8, 9]),
Vectors.dense([10, 11, 12])])
+ >>> rm = RowMatrix(rows, 7, 6)
+ >>> int(rm.numRows())
+ 7
+ """
+ return self._jrm.call("numRows")
+
+ def numCols(self):
+ """
+ Get or compute the number of cols.
+
+ >>> rows = sc.parallelize([Vectors.dense([1, 2, 3]),
Vectors.dense([4, 5, 6]),
+ ... Vectors.dense([7, 8, 9]),
Vectors.dense([10, 11, 12])])
+ >>> rm = RowMatrix(rows)
+ >>> int(rm.numCols())
+ 3
+
+ >>> rows = sc.parallelize([Vectors.dense([1, 2, 3]),
Vectors.dense([4, 5, 6]),
+ ... Vectors.dense([7, 8, 9]),
Vectors.dense([10, 11, 12])])
+ >>> rm = RowMatrix(rows, 7, 6)
+ >>> int(rm.numCols())
+ 6
+ """
+ return self._jrm.call("numCols")
+
+
+class IndexedRow(object):
+ """
+ Represents a row of an IndexedRowMatrix.
+
+ Just a wrapper over a (long, Vector) tuple.
+
+ .. note:: Experimental
+ """
+ def __init__(self, index, vector):
+ self.index = long(index)
+ self.vector = _convert_to_vector(vector)
+
+ def __repr__(self):
+ return "IndexedRow(%s, %s)" % (self.index, self.vector)
+
+
+class IndexedRowMatrix(DistributedMatrix):
+ """
+ Represents a row-oriented distributed Matrix with indexed rows.
+
+ .. note:: Experimental
+ """
+ def __init__(self, rows, numRows=0, numCols=0):
+ """
+ Create a wrapper over a Java IndexedRowMatrix.
+
+ :param rows: An RDD of IndexedRows or (long, Vector) tuples.
+ """
+ if not isinstance(rows, RDD):
+ raise TypeError("rows should be an RDD object, got %s" %
type(rows))
+ first = rows.first()
+ if not (isinstance(first, IndexedRow) or
+ (isinstance(first, tuple) and len(first) == 2 and
+ isinstance(first[0], (long, int)) and
isinstance(first[1], Vector))):
--- End diff --
Actually, just doing
rows.map(lambda row: IndexedRow(*row))
would suffice since there is casting in `IndexedRow`
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