Source code for pyspark.mllib.linalg.distributed

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"""
Package for distributed linear algebra.
"""

import sys

if sys.version >= '3':
    long = int

from py4j.java_gateway import JavaObject

from pyspark import RDD
from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
from pyspark.mllib.linalg import _convert_to_vector, Matrix


__all__ = ['DistributedMatrix', 'RowMatrix', 'IndexedRow',
           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix',
           'BlockMatrix']


[docs]class DistributedMatrix(object): """ .. note:: Experimental Represents a distributively stored matrix backed by one or more RDDs. """
[docs] def numRows(self): """Get or compute the number of rows.""" raise NotImplementedError
[docs] def numCols(self): """Get or compute the number of cols.""" raise NotImplementedError
[docs]class RowMatrix(DistributedMatrix): """ .. note:: Experimental Represents a row-oriented distributed Matrix with no meaningful row indices. :param rows: An RDD of vectors. :param numRows: Number of rows in the matrix. A non-positive value means unknown, at which point the number of rows will be determined by the number of records in the `rows` RDD. :param numCols: Number of columns in the matrix. A non-positive value means unknown, at which point the number of columns will be determined by the size of the first row. """ def __init__(self, rows, numRows=0, numCols=0): """ Note: This docstring is not shown publicly. Create a wrapper over a Java RowMatrix. Publicly, we require that `rows` be an RDD. However, for internal usage, `rows` can also be a Java RowMatrix object, in which case we can wrap it directly. This assists in clean matrix conversions. >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]]) >>> mat = RowMatrix(rows) >>> mat_diff = RowMatrix(rows) >>> (mat_diff._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) False >>> mat_same = RowMatrix(mat._java_matrix_wrapper._java_model) >>> (mat_same._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) True """ if isinstance(rows, RDD): rows = rows.map(_convert_to_vector) java_matrix = callMLlibFunc("createRowMatrix", rows, long(numRows), int(numCols)) elif (isinstance(rows, JavaObject) and rows.getClass().getSimpleName() == "RowMatrix"): java_matrix = rows else: raise TypeError("rows should be an RDD of vectors, got %s" % type(rows)) self._java_matrix_wrapper = JavaModelWrapper(java_matrix) @property
[docs] def rows(self): """ Rows of the RowMatrix stored as an RDD of vectors. >>> mat = RowMatrix(sc.parallelize([[1, 2, 3], [4, 5, 6]])) >>> rows = mat.rows >>> rows.first() DenseVector([1.0, 2.0, 3.0]) """ return self._java_matrix_wrapper.call("rows")
[docs] def numRows(self): """ Get or compute the number of rows. >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6], ... [7, 8, 9], [10, 11, 12]]) >>> mat = RowMatrix(rows) >>> print(mat.numRows()) 4 >>> mat = RowMatrix(rows, 7, 6) >>> print(mat.numRows()) 7 """ return self._java_matrix_wrapper.call("numRows")
[docs] def numCols(self): """ Get or compute the number of cols. >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6], ... [7, 8, 9], [10, 11, 12]]) >>> mat = RowMatrix(rows) >>> print(mat.numCols()) 3 >>> mat = RowMatrix(rows, 7, 6) >>> print(mat.numCols()) 6 """ return self._java_matrix_wrapper.call("numCols")
[docs]class IndexedRow(object): """ .. note:: Experimental Represents a row of an IndexedRowMatrix. Just a wrapper over a (long, vector) tuple. :param index: The index for the given row. :param vector: The row in the matrix at the given index. """ 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)
def _convert_to_indexed_row(row): if isinstance(row, IndexedRow): return row elif isinstance(row, tuple) and len(row) == 2: return IndexedRow(*row) else: raise TypeError("Cannot convert type %s into IndexedRow" % type(row))
[docs]class IndexedRowMatrix(DistributedMatrix): """ .. note:: Experimental Represents a row-oriented distributed Matrix with indexed rows. :param rows: An RDD of IndexedRows or (long, vector) tuples. :param numRows: Number of rows in the matrix. A non-positive value means unknown, at which point the number of rows will be determined by the max row index plus one. :param numCols: Number of columns in the matrix. A non-positive value means unknown, at which point the number of columns will be determined by the size of the first row. """ def __init__(self, rows, numRows=0, numCols=0): """ Note: This docstring is not shown publicly. Create a wrapper over a Java IndexedRowMatrix. Publicly, we require that `rows` be an RDD. However, for internal usage, `rows` can also be a Java IndexedRowMatrix object, in which case we can wrap it directly. This assists in clean matrix conversions. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(1, [4, 5, 6])]) >>> mat = IndexedRowMatrix(rows) >>> mat_diff = IndexedRowMatrix(rows) >>> (mat_diff._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) False >>> mat_same = IndexedRowMatrix(mat._java_matrix_wrapper._java_model) >>> (mat_same._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) True """ if isinstance(rows, RDD): rows = rows.map(_convert_to_indexed_row) # We use DataFrames for serialization of IndexedRows from # Python, so first convert the RDD to a DataFrame on this # side. 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. java_matrix = callMLlibFunc("createIndexedRowMatrix", rows.toDF(), long(numRows), int(numCols)) elif (isinstance(rows, JavaObject) and rows.getClass().getSimpleName() == "IndexedRowMatrix"): java_matrix = rows else: raise TypeError("rows should be an RDD of IndexedRows or (long, vector) tuples, " "got %s" % type(rows)) self._java_matrix_wrapper = JavaModelWrapper(java_matrix) @property
[docs] def rows(self): """ Rows of the IndexedRowMatrix stored as an RDD of IndexedRows. >>> mat = IndexedRowMatrix(sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(1, [4, 5, 6])])) >>> rows = mat.rows >>> rows.first() IndexedRow(0, [1.0,2.0,3.0]) """ # We use DataFrames for serialization of IndexedRows from # Java, so we first convert the RDD of rows to a DataFrame # on the Scala/Java side. Then we map each Row in the # DataFrame back to an IndexedRow on this side. rows_df = callMLlibFunc("getIndexedRows", self._java_matrix_wrapper._java_model) rows = rows_df.map(lambda row: IndexedRow(row[0], row[1])) return rows
[docs] def numRows(self): """ Get or compute the number of rows. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(1, [4, 5, 6]), ... IndexedRow(2, [7, 8, 9]), ... IndexedRow(3, [10, 11, 12])]) >>> mat = IndexedRowMatrix(rows) >>> print(mat.numRows()) 4 >>> mat = IndexedRowMatrix(rows, 7, 6) >>> print(mat.numRows()) 7 """ return self._java_matrix_wrapper.call("numRows")
[docs] def numCols(self): """ Get or compute the number of cols. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(1, [4, 5, 6]), ... IndexedRow(2, [7, 8, 9]), ... IndexedRow(3, [10, 11, 12])]) >>> mat = IndexedRowMatrix(rows) >>> print(mat.numCols()) 3 >>> mat = IndexedRowMatrix(rows, 7, 6) >>> print(mat.numCols()) 6 """ return self._java_matrix_wrapper.call("numCols")
[docs] def toRowMatrix(self): """ Convert this matrix to a RowMatrix. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(6, [4, 5, 6])]) >>> mat = IndexedRowMatrix(rows).toRowMatrix() >>> mat.rows.collect() [DenseVector([1.0, 2.0, 3.0]), DenseVector([4.0, 5.0, 6.0])] """ java_row_matrix = self._java_matrix_wrapper.call("toRowMatrix") return RowMatrix(java_row_matrix)
[docs] def toCoordinateMatrix(self): """ Convert this matrix to a CoordinateMatrix. >>> rows = sc.parallelize([IndexedRow(0, [1, 0]), ... IndexedRow(6, [0, 5])]) >>> mat = IndexedRowMatrix(rows).toCoordinateMatrix() >>> mat.entries.take(3) [MatrixEntry(0, 0, 1.0), MatrixEntry(0, 1, 0.0), MatrixEntry(6, 0, 0.0)] """ java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix") return CoordinateMatrix(java_coordinate_matrix)
[docs] def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024): """ Convert this matrix to a BlockMatrix. :param rowsPerBlock: Number of rows that make up each block. The blocks forming the final rows are not required to have the given number of rows. :param colsPerBlock: Number of columns that make up each block. The blocks forming the final columns are not required to have the given number of columns. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(6, [4, 5, 6])]) >>> mat = IndexedRowMatrix(rows).toBlockMatrix() >>> # This IndexedRowMatrix will have 7 effective rows, due to >>> # the highest row index being 6, and the ensuing >>> # BlockMatrix will have 7 rows as well. >>> print(mat.numRows()) 7 >>> print(mat.numCols()) 3 """ java_block_matrix = self._java_matrix_wrapper.call("toBlockMatrix", rowsPerBlock, colsPerBlock) return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
[docs]class MatrixEntry(object): """ .. note:: Experimental Represents an entry of a CoordinateMatrix. Just a wrapper over a (long, long, float) tuple. :param i: The row index of the matrix. :param j: The column index of the matrix. :param value: The (i, j)th entry of the matrix, as a float. """ def __init__(self, i, j, value): self.i = long(i) self.j = long(j) self.value = float(value) def __repr__(self): return "MatrixEntry(%s, %s, %s)" % (self.i, self.j, self.value)
def _convert_to_matrix_entry(entry): if isinstance(entry, MatrixEntry): return entry elif isinstance(entry, tuple) and len(entry) == 3: return MatrixEntry(*entry) else: raise TypeError("Cannot convert type %s into MatrixEntry" % type(entry))
[docs]class CoordinateMatrix(DistributedMatrix): """ .. note:: Experimental Represents a matrix in coordinate format. :param entries: An RDD of MatrixEntry inputs or (long, long, float) tuples. :param numRows: Number of rows in the matrix. A non-positive value means unknown, at which point the number of rows will be determined by the max row index plus one. :param numCols: Number of columns in the matrix. A non-positive value means unknown, at which point the number of columns will be determined by the max row index plus one. """ def __init__(self, entries, numRows=0, numCols=0): """ Note: This docstring is not shown publicly. Create a wrapper over a Java CoordinateMatrix. Publicly, we require that `rows` be an RDD. However, for internal usage, `rows` can also be a Java CoordinateMatrix object, in which case we can wrap it directly. This assists in clean matrix conversions. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(6, 4, 2.1)]) >>> mat = CoordinateMatrix(entries) >>> mat_diff = CoordinateMatrix(entries) >>> (mat_diff._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) False >>> mat_same = CoordinateMatrix(mat._java_matrix_wrapper._java_model) >>> (mat_same._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) True """ if isinstance(entries, RDD): entries = entries.map(_convert_to_matrix_entry) # We use DataFrames for serialization of MatrixEntry entries # from Python, so first convert the RDD to a DataFrame on # this side. 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. java_matrix = callMLlibFunc("createCoordinateMatrix", entries.toDF(), long(numRows), long(numCols)) elif (isinstance(entries, JavaObject) and entries.getClass().getSimpleName() == "CoordinateMatrix"): java_matrix = entries else: raise TypeError("entries should be an RDD of MatrixEntry entries or " "(long, long, float) tuples, got %s" % type(entries)) self._java_matrix_wrapper = JavaModelWrapper(java_matrix) @property
[docs] def entries(self): """ Entries of the CoordinateMatrix stored as an RDD of MatrixEntries. >>> mat = CoordinateMatrix(sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(6, 4, 2.1)])) >>> entries = mat.entries >>> entries.first() MatrixEntry(0, 0, 1.2) """ # We use DataFrames for serialization of MatrixEntry entries # from Java, so we first convert the RDD of entries to a # DataFrame on the Scala/Java side. Then we map each Row in # the DataFrame back to a MatrixEntry on this side. entries_df = callMLlibFunc("getMatrixEntries", self._java_matrix_wrapper._java_model) entries = entries_df.map(lambda row: MatrixEntry(row[0], row[1], row[2])) return entries
[docs] def numRows(self): """ Get or compute the number of rows. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(1, 0, 2), ... MatrixEntry(2, 1, 3.7)]) >>> mat = CoordinateMatrix(entries) >>> print(mat.numRows()) 3 >>> mat = CoordinateMatrix(entries, 7, 6) >>> print(mat.numRows()) 7 """ return self._java_matrix_wrapper.call("numRows")
[docs] def numCols(self): """ Get or compute the number of cols. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(1, 0, 2), ... MatrixEntry(2, 1, 3.7)]) >>> mat = CoordinateMatrix(entries) >>> print(mat.numCols()) 2 >>> mat = CoordinateMatrix(entries, 7, 6) >>> print(mat.numCols()) 6 """ return self._java_matrix_wrapper.call("numCols")
[docs] def toRowMatrix(self): """ Convert this matrix to a RowMatrix. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(6, 4, 2.1)]) >>> mat = CoordinateMatrix(entries).toRowMatrix() >>> # This CoordinateMatrix will have 7 effective rows, due to >>> # the highest row index being 6, but the ensuing RowMatrix >>> # will only have 2 rows since there are only entries on 2 >>> # unique rows. >>> print(mat.numRows()) 2 >>> # This CoordinateMatrix will have 5 columns, due to the >>> # highest column index being 4, and the ensuing RowMatrix >>> # will have 5 columns as well. >>> print(mat.numCols()) 5 """ java_row_matrix = self._java_matrix_wrapper.call("toRowMatrix") return RowMatrix(java_row_matrix)
[docs] def toIndexedRowMatrix(self): """ Convert this matrix to an IndexedRowMatrix. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(6, 4, 2.1)]) >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix() >>> # This CoordinateMatrix will have 7 effective rows, due to >>> # the highest row index being 6, and the ensuing >>> # IndexedRowMatrix will have 7 rows as well. >>> print(mat.numRows()) 7 >>> # This CoordinateMatrix will have 5 columns, due to the >>> # highest column index being 4, and the ensuing >>> # IndexedRowMatrix will have 5 columns as well. >>> print(mat.numCols()) 5 """ java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix") return IndexedRowMatrix(java_indexed_row_matrix)
[docs] def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024): """ Convert this matrix to a BlockMatrix. :param rowsPerBlock: Number of rows that make up each block. The blocks forming the final rows are not required to have the given number of rows. :param colsPerBlock: Number of columns that make up each block. The blocks forming the final columns are not required to have the given number of columns. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(6, 4, 2.1)]) >>> mat = CoordinateMatrix(entries).toBlockMatrix() >>> # This CoordinateMatrix will have 7 effective rows, due to >>> # the highest row index being 6, and the ensuing >>> # BlockMatrix will have 7 rows as well. >>> print(mat.numRows()) 7 >>> # This CoordinateMatrix will have 5 columns, due to the >>> # highest column index being 4, and the ensuing >>> # BlockMatrix will have 5 columns as well. >>> print(mat.numCols()) 5 """ java_block_matrix = self._java_matrix_wrapper.call("toBlockMatrix", rowsPerBlock, colsPerBlock) return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
def _convert_to_matrix_block_tuple(block): if (isinstance(block, tuple) and len(block) == 2 and isinstance(block[0], tuple) and len(block[0]) == 2 and isinstance(block[1], Matrix)): blockRowIndex = int(block[0][0]) blockColIndex = int(block[0][1]) subMatrix = block[1] return ((blockRowIndex, blockColIndex), subMatrix) else: raise TypeError("Cannot convert type %s into a sub-matrix block tuple" % type(block))
[docs]class BlockMatrix(DistributedMatrix): """ .. note:: Experimental Represents a distributed matrix in blocks of local matrices. :param blocks: An RDD of sub-matrix blocks ((blockRowIndex, blockColIndex), sub-matrix) that form this distributed matrix. If multiple blocks with the same index exist, the results for operations like add and multiply will be unpredictable. :param rowsPerBlock: Number of rows that make up each block. The blocks forming the final rows are not required to have the given number of rows. :param colsPerBlock: Number of columns that make up each block. The blocks forming the final columns are not required to have the given number of columns. :param numRows: Number of rows of this matrix. If the supplied value is less than or equal to zero, the number of rows will be calculated when `numRows` is invoked. :param numCols: Number of columns of this matrix. If the supplied value is less than or equal to zero, the number of columns will be calculated when `numCols` is invoked. """ def __init__(self, blocks, rowsPerBlock, colsPerBlock, numRows=0, numCols=0): """ Note: This docstring is not shown publicly. Create a wrapper over a Java BlockMatrix. Publicly, we require that `blocks` be an RDD. However, for internal usage, `blocks` can also be a Java BlockMatrix object, in which case we can wrap it directly. This assists in clean matrix conversions. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> mat_diff = BlockMatrix(blocks, 3, 2) >>> (mat_diff._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) False >>> mat_same = BlockMatrix(mat._java_matrix_wrapper._java_model, 3, 2) >>> (mat_same._java_matrix_wrapper._java_model == ... mat._java_matrix_wrapper._java_model) True """ if isinstance(blocks, RDD): blocks = blocks.map(_convert_to_matrix_block_tuple) # We use DataFrames for serialization of sub-matrix blocks # from Python, so first convert the RDD to a DataFrame on # this side. This will convert each sub-matrix block # tuple to a Row containing the 'blockRowIndex', # 'blockColIndex', and 'subMatrix' values, which can # each be easily serialized. We will convert back to # ((blockRowIndex, blockColIndex), sub-matrix) tuples on # the Scala side. java_matrix = callMLlibFunc("createBlockMatrix", blocks.toDF(), int(rowsPerBlock), int(colsPerBlock), long(numRows), long(numCols)) elif (isinstance(blocks, JavaObject) and blocks.getClass().getSimpleName() == "BlockMatrix"): java_matrix = blocks else: raise TypeError("blocks should be an RDD of sub-matrix blocks as " "((int, int), matrix) tuples, got %s" % type(blocks)) self._java_matrix_wrapper = JavaModelWrapper(java_matrix) @property
[docs] def blocks(self): """ The RDD of sub-matrix blocks ((blockRowIndex, blockColIndex), sub-matrix) that form this distributed matrix. >>> mat = BlockMatrix( ... sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]), 3, 2) >>> blocks = mat.blocks >>> blocks.first() ((0, 0), DenseMatrix(3, 2, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 0)) """ # We use DataFrames for serialization of sub-matrix blocks # from Java, so we first convert the RDD of blocks to a # DataFrame on the Scala/Java side. Then we map each Row in # the DataFrame back to a sub-matrix block on this side. blocks_df = callMLlibFunc("getMatrixBlocks", self._java_matrix_wrapper._java_model) blocks = blocks_df.map(lambda row: ((row[0][0], row[0][1]), row[1])) return blocks
@property
[docs] def rowsPerBlock(self): """ Number of rows that make up each block. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> mat.rowsPerBlock 3 """ return self._java_matrix_wrapper.call("rowsPerBlock")
@property
[docs] def colsPerBlock(self): """ Number of columns that make up each block. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> mat.colsPerBlock 2 """ return self._java_matrix_wrapper.call("colsPerBlock")
@property
[docs] def numRowBlocks(self): """ Number of rows of blocks in the BlockMatrix. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> mat.numRowBlocks 2 """ return self._java_matrix_wrapper.call("numRowBlocks")
@property
[docs] def numColBlocks(self): """ Number of columns of blocks in the BlockMatrix. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> mat.numColBlocks 1 """ return self._java_matrix_wrapper.call("numColBlocks")
[docs] def numRows(self): """ Get or compute the number of rows. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> print(mat.numRows()) 6 >>> mat = BlockMatrix(blocks, 3, 2, 7, 6) >>> print(mat.numRows()) 7 """ return self._java_matrix_wrapper.call("numRows")
[docs] def numCols(self): """ Get or compute the number of cols. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2) >>> print(mat.numCols()) 2 >>> mat = BlockMatrix(blocks, 3, 2, 7, 6) >>> print(mat.numCols()) 6 """ return self._java_matrix_wrapper.call("numCols")
[docs] def add(self, other): """ Adds two block matrices together. The matrices must have the same size and matching `rowsPerBlock` and `colsPerBlock` values. If one of the sub matrix blocks that are being added is a SparseMatrix, the resulting sub matrix block will also be a SparseMatrix, even if it is being added to a DenseMatrix. If two dense sub matrix blocks are added, the output block will also be a DenseMatrix. >>> dm1 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6]) >>> dm2 = Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]) >>> sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 1, 2], [7, 11, 12]) >>> blocks1 = sc.parallelize([((0, 0), dm1), ((1, 0), dm2)]) >>> blocks2 = sc.parallelize([((0, 0), dm1), ((1, 0), dm2)]) >>> blocks3 = sc.parallelize([((0, 0), sm), ((1, 0), dm2)]) >>> mat1 = BlockMatrix(blocks1, 3, 2) >>> mat2 = BlockMatrix(blocks2, 3, 2) >>> mat3 = BlockMatrix(blocks3, 3, 2) >>> mat1.add(mat2).toLocalMatrix() DenseMatrix(6, 2, [2.0, 4.0, 6.0, 14.0, 16.0, 18.0, 8.0, 10.0, 12.0, 20.0, 22.0, 24.0], 0) >>> mat1.add(mat3).toLocalMatrix() DenseMatrix(6, 2, [8.0, 2.0, 3.0, 14.0, 16.0, 18.0, 4.0, 16.0, 18.0, 20.0, 22.0, 24.0], 0) """ if not isinstance(other, BlockMatrix): raise TypeError("Other should be a BlockMatrix, got %s" % type(other)) other_java_block_matrix = other._java_matrix_wrapper._java_model java_block_matrix = self._java_matrix_wrapper.call("add", other_java_block_matrix) return BlockMatrix(java_block_matrix, self.rowsPerBlock, self.colsPerBlock)
[docs] def multiply(self, other): """ Left multiplies this BlockMatrix by `other`, another BlockMatrix. The `colsPerBlock` of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains any SparseMatrix blocks, they will have to be converted to DenseMatrix blocks. The output BlockMatrix will only consist of DenseMatrix blocks. This may cause some performance issues until support for multiplying two sparse matrices is added. >>> dm1 = Matrices.dense(2, 3, [1, 2, 3, 4, 5, 6]) >>> dm2 = Matrices.dense(2, 3, [7, 8, 9, 10, 11, 12]) >>> dm3 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6]) >>> dm4 = Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]) >>> sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 1, 2], [7, 11, 12]) >>> blocks1 = sc.parallelize([((0, 0), dm1), ((0, 1), dm2)]) >>> blocks2 = sc.parallelize([((0, 0), dm3), ((1, 0), dm4)]) >>> blocks3 = sc.parallelize([((0, 0), sm), ((1, 0), dm4)]) >>> mat1 = BlockMatrix(blocks1, 2, 3) >>> mat2 = BlockMatrix(blocks2, 3, 2) >>> mat3 = BlockMatrix(blocks3, 3, 2) >>> mat1.multiply(mat2).toLocalMatrix() DenseMatrix(2, 2, [242.0, 272.0, 350.0, 398.0], 0) >>> mat1.multiply(mat3).toLocalMatrix() DenseMatrix(2, 2, [227.0, 258.0, 394.0, 450.0], 0) """ if not isinstance(other, BlockMatrix): raise TypeError("Other should be a BlockMatrix, got %s" % type(other)) other_java_block_matrix = other._java_matrix_wrapper._java_model java_block_matrix = self._java_matrix_wrapper.call("multiply", other_java_block_matrix) return BlockMatrix(java_block_matrix, self.rowsPerBlock, self.colsPerBlock)
[docs] def toLocalMatrix(self): """ Collect the distributed matrix on the driver as a DenseMatrix. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2).toLocalMatrix() >>> # This BlockMatrix will have 6 effective rows, due to >>> # having two sub-matrix blocks stacked, each with 3 rows. >>> # The ensuing DenseMatrix will also have 6 rows. >>> print(mat.numRows) 6 >>> # This BlockMatrix will have 2 effective columns, due to >>> # having two sub-matrix blocks stacked, each with 2 >>> # columns. The ensuing DenseMatrix will also have 2 columns. >>> print(mat.numCols) 2 """ return self._java_matrix_wrapper.call("toLocalMatrix")
[docs] def toIndexedRowMatrix(self): """ Convert this matrix to an IndexedRowMatrix. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), ... ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]) >>> mat = BlockMatrix(blocks, 3, 2).toIndexedRowMatrix() >>> # This BlockMatrix will have 6 effective rows, due to >>> # having two sub-matrix blocks stacked, each with 3 rows. >>> # The ensuing IndexedRowMatrix will also have 6 rows. >>> print(mat.numRows()) 6 >>> # This BlockMatrix will have 2 effective columns, due to >>> # having two sub-matrix blocks stacked, each with 2 columns. >>> # The ensuing IndexedRowMatrix will also have 2 columns. >>> print(mat.numCols()) 2 """ java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix") return IndexedRowMatrix(java_indexed_row_matrix)
[docs] def toCoordinateMatrix(self): """ Convert this matrix to a CoordinateMatrix. >>> blocks = sc.parallelize([((0, 0), Matrices.dense(1, 2, [1, 2])), ... ((1, 0), Matrices.dense(1, 2, [7, 8]))]) >>> mat = BlockMatrix(blocks, 1, 2).toCoordinateMatrix() >>> mat.entries.take(3) [MatrixEntry(0, 0, 1.0), MatrixEntry(0, 1, 2.0), MatrixEntry(1, 0, 7.0)] """ java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix") return CoordinateMatrix(java_coordinate_matrix)
def _test(): import doctest from pyspark import SparkContext from pyspark.sql import SQLContext from pyspark.mllib.linalg import Matrices import pyspark.mllib.linalg.distributed globs = pyspark.mllib.linalg.distributed.__dict__.copy() globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2) globs['sqlContext'] = SQLContext(globs['sc']) globs['Matrices'] = Matrices (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()