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, since
from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
from pyspark.mllib.linalg import _convert_to_vector, DenseMatrix, Matrix, QRDecomposition
from pyspark.mllib.stat import MultivariateStatisticalSummary
from pyspark.sql import DataFrame
from pyspark.storagelevel import StorageLevel


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


[docs]class DistributedMatrix(object): """ 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): """ Represents a row-oriented distributed Matrix with no meaningful row indices. :param rows: An RDD or DataFrame of vectors. If a DataFrame is provided, it must have a single vector typed column. :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 or DataFrame. 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, DataFrame): 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 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] @since('2.0.0') def computeColumnSummaryStatistics(self): """ Computes column-wise summary statistics. :return: :class:`MultivariateStatisticalSummary` object containing column-wise summary statistics. >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]]) >>> mat = RowMatrix(rows) >>> colStats = mat.computeColumnSummaryStatistics() >>> colStats.mean() array([ 2.5, 3.5, 4.5]) """ java_col_stats = self._java_matrix_wrapper.call("computeColumnSummaryStatistics") return MultivariateStatisticalSummary(java_col_stats)
[docs] @since('2.0.0') def computeCovariance(self): """ Computes the covariance matrix, treating each row as an observation. .. note:: This cannot be computed on matrices with more than 65535 columns. >>> rows = sc.parallelize([[1, 2], [2, 1]]) >>> mat = RowMatrix(rows) >>> mat.computeCovariance() DenseMatrix(2, 2, [0.5, -0.5, -0.5, 0.5], 0) """ return self._java_matrix_wrapper.call("computeCovariance")
[docs] @since('2.0.0') def computeGramianMatrix(self): """ Computes the Gramian matrix `A^T A`. .. note:: This cannot be computed on matrices with more than 65535 columns. >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]]) >>> mat = RowMatrix(rows) >>> mat.computeGramianMatrix() DenseMatrix(3, 3, [17.0, 22.0, 27.0, 22.0, 29.0, 36.0, 27.0, 36.0, 45.0], 0) """ return self._java_matrix_wrapper.call("computeGramianMatrix")
[docs] @since('2.0.0') def columnSimilarities(self, threshold=0.0): """ Compute similarities between columns of this matrix. The threshold parameter is a trade-off knob between estimate quality and computational cost. The default threshold setting of 0 guarantees deterministically correct results, but uses the brute-force approach of computing normalized dot products. Setting the threshold to positive values uses a sampling approach and incurs strictly less computational cost than the brute-force approach. However the similarities computed will be estimates. The sampling guarantees relative-error correctness for those pairs of columns that have similarity greater than the given similarity threshold. To describe the guarantee, we set some notation: * Let A be the smallest in magnitude non-zero element of this matrix. * Let B be the largest in magnitude non-zero element of this matrix. * Let L be the maximum number of non-zeros per row. For example, for {0,1} matrices: A=B=1. Another example, for the Netflix matrix: A=1, B=5 For those column pairs that are above the threshold, the computed similarity is correct to within 20% relative error with probability at least 1 - (0.981)^10/B^ The shuffle size is bounded by the *smaller* of the following two expressions: * O(n log(n) L / (threshold * A)) * O(m L^2^) The latter is the cost of the brute-force approach, so for non-zero thresholds, the cost is always cheaper than the brute-force approach. :param: threshold: Set to 0 for deterministic guaranteed correctness. Similarities above this threshold are estimated with the cost vs estimate quality trade-off described above. :return: An n x n sparse upper-triangular CoordinateMatrix of cosine similarities between columns of this matrix. >>> rows = sc.parallelize([[1, 2], [1, 5]]) >>> mat = RowMatrix(rows) >>> sims = mat.columnSimilarities() >>> sims.entries.first().value 0.91914503... """ java_sims_mat = self._java_matrix_wrapper.call("columnSimilarities", float(threshold)) return CoordinateMatrix(java_sims_mat)
[docs] @since('2.0.0') def tallSkinnyQR(self, computeQ=False): """ Compute the QR decomposition of this RowMatrix. The implementation is designed to optimize the QR decomposition (factorization) for the RowMatrix of a tall and skinny shape. Reference: Paul G. Constantine, David F. Gleich. "Tall and skinny QR factorizations in MapReduce architectures" ([[https://doi.org/10.1145/1996092.1996103]]) :param: computeQ: whether to computeQ :return: QRDecomposition(Q: RowMatrix, R: Matrix), where Q = None if computeQ = false. >>> rows = sc.parallelize([[3, -6], [4, -8], [0, 1]]) >>> mat = RowMatrix(rows) >>> decomp = mat.tallSkinnyQR(True) >>> Q = decomp.Q >>> R = decomp.R >>> # Test with absolute values >>> absQRows = Q.rows.map(lambda row: abs(row.toArray()).tolist()) >>> absQRows.collect() [[0.6..., 0.0], [0.8..., 0.0], [0.0, 1.0]] >>> # Test with absolute values >>> abs(R.toArray()).tolist() [[5.0, 10.0], [0.0, 1.0]] """ decomp = JavaModelWrapper(self._java_matrix_wrapper.call("tallSkinnyQR", computeQ)) if computeQ: java_Q = decomp.call("Q") Q = RowMatrix(java_Q) else: Q = None R = decomp.call("R") return QRDecomposition(Q, R)
[docs] @since('2.2.0') def computeSVD(self, k, computeU=False, rCond=1e-9): """ Computes the singular value decomposition of the RowMatrix. The given row matrix A of dimension (m X n) is decomposed into U * s * V'T where * U: (m X k) (left singular vectors) is a RowMatrix whose columns are the eigenvectors of (A X A') * s: DenseVector consisting of square root of the eigenvalues (singular values) in descending order. * v: (n X k) (right singular vectors) is a Matrix whose columns are the eigenvectors of (A' X A) For more specific details on implementation, please refer the Scala documentation. :param k: Number of leading singular values to keep (`0 < k <= n`). It might return less than k if there are numerically zero singular values or there are not enough Ritz values converged before the maximum number of Arnoldi update iterations is reached (in case that matrix A is ill-conditioned). :param computeU: Whether or not to compute U. If set to be True, then U is computed by A * V * s^-1 :param rCond: Reciprocal condition number. All singular values smaller than rCond * s[0] are treated as zero where s[0] is the largest singular value. :returns: :py:class:`SingularValueDecomposition` >>> rows = sc.parallelize([[3, 1, 1], [-1, 3, 1]]) >>> rm = RowMatrix(rows) >>> svd_model = rm.computeSVD(2, True) >>> svd_model.U.rows.collect() [DenseVector([-0.7071, 0.7071]), DenseVector([-0.7071, -0.7071])] >>> svd_model.s DenseVector([3.4641, 3.1623]) >>> svd_model.V DenseMatrix(3, 2, [-0.4082, -0.8165, -0.4082, 0.8944, -0.4472, 0.0], 0) """ j_model = self._java_matrix_wrapper.call( "computeSVD", int(k), bool(computeU), float(rCond)) return SingularValueDecomposition(j_model)
[docs] @since('2.2.0') def computePrincipalComponents(self, k): """ Computes the k principal components of the given row matrix .. note:: This cannot be computed on matrices with more than 65535 columns. :param k: Number of principal components to keep. :returns: :py:class:`pyspark.mllib.linalg.DenseMatrix` >>> rows = sc.parallelize([[1, 2, 3], [2, 4, 5], [3, 6, 1]]) >>> rm = RowMatrix(rows) >>> # Returns the two principal components of rm >>> pca = rm.computePrincipalComponents(2) >>> pca DenseMatrix(3, 2, [-0.349, -0.6981, 0.6252, -0.2796, -0.5592, -0.7805], 0) >>> # Transform into new dimensions with the greatest variance. >>> rm.multiply(pca).rows.collect() # doctest: +NORMALIZE_WHITESPACE [DenseVector([0.1305, -3.7394]), DenseVector([-0.3642, -6.6983]), \ DenseVector([-4.6102, -4.9745])] """ return self._java_matrix_wrapper.call("computePrincipalComponents", k)
[docs] @since('2.2.0') def multiply(self, matrix): """ Multiply this matrix by a local dense matrix on the right. :param matrix: a local dense matrix whose number of rows must match the number of columns of this matrix :returns: :py:class:`RowMatrix` >>> rm = RowMatrix(sc.parallelize([[0, 1], [2, 3]])) >>> rm.multiply(DenseMatrix(2, 2, [0, 2, 1, 3])).rows.collect() [DenseVector([2.0, 3.0]), DenseVector([6.0, 11.0])] """ if not isinstance(matrix, DenseMatrix): raise ValueError("Only multiplication with DenseMatrix " "is supported.") j_model = self._java_matrix_wrapper.call("multiply", matrix) return RowMatrix(j_model)
[docs]class SingularValueDecomposition(JavaModelWrapper): """ Represents singular value decomposition (SVD) factors. .. versionadded:: 2.2.0 """ @property @since('2.2.0') def U(self): """ Returns a distributed matrix whose columns are the left singular vectors of the SingularValueDecomposition if computeU was set to be True. """ u = self.call("U") if u is not None: mat_name = u.getClass().getSimpleName() if mat_name == "RowMatrix": return RowMatrix(u) elif mat_name == "IndexedRowMatrix": return IndexedRowMatrix(u) else: raise TypeError("Expected RowMatrix/IndexedRowMatrix got %s" % mat_name) @property @since('2.2.0') def s(self): """ Returns a DenseVector with singular values in descending order. """ return self.call("s") @property @since('2.2.0') def V(self): """ Returns a DenseMatrix whose columns are the right singular vectors of the SingularValueDecomposition. """ return self.call("V")
[docs]class IndexedRow(object): """ 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): """ Represents a row-oriented distributed Matrix with indexed rows. :param rows: An RDD of IndexedRows or (long, vector) tuples or a DataFrame consisting of a long typed column of indices and a vector typed column. :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 or DataFrame. 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, DataFrame): java_matrix = callMLlibFunc("createIndexedRowMatrix", rows, 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 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.rdd.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 columnSimilarities(self): """ Compute all cosine similarities between columns. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(6, [4, 5, 6])]) >>> mat = IndexedRowMatrix(rows) >>> cs = mat.columnSimilarities() >>> print(cs.numCols()) 3 """ java_coordinate_matrix = self._java_matrix_wrapper.call("columnSimilarities") return CoordinateMatrix(java_coordinate_matrix)
[docs] @since('2.0.0') def computeGramianMatrix(self): """ Computes the Gramian matrix `A^T A`. .. note:: This cannot be computed on matrices with more than 65535 columns. >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]), ... IndexedRow(1, [4, 5, 6])]) >>> mat = IndexedRowMatrix(rows) >>> mat.computeGramianMatrix() DenseMatrix(3, 3, [17.0, 22.0, 27.0, 22.0, 29.0, 36.0, 27.0, 36.0, 45.0], 0) """ return self._java_matrix_wrapper.call("computeGramianMatrix")
[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] @since('2.2.0') def computeSVD(self, k, computeU=False, rCond=1e-9): """ Computes the singular value decomposition of the IndexedRowMatrix. The given row matrix A of dimension (m X n) is decomposed into U * s * V'T where * U: (m X k) (left singular vectors) is a IndexedRowMatrix whose columns are the eigenvectors of (A X A') * s: DenseVector consisting of square root of the eigenvalues (singular values) in descending order. * v: (n X k) (right singular vectors) is a Matrix whose columns are the eigenvectors of (A' X A) For more specific details on implementation, please refer the scala documentation. :param k: Number of leading singular values to keep (`0 < k <= n`). It might return less than k if there are numerically zero singular values or there are not enough Ritz values converged before the maximum number of Arnoldi update iterations is reached (in case that matrix A is ill-conditioned). :param computeU: Whether or not to compute U. If set to be True, then U is computed by A * V * s^-1 :param rCond: Reciprocal condition number. All singular values smaller than rCond * s[0] are treated as zero where s[0] is the largest singular value. :returns: SingularValueDecomposition object >>> rows = [(0, (3, 1, 1)), (1, (-1, 3, 1))] >>> irm = IndexedRowMatrix(sc.parallelize(rows)) >>> svd_model = irm.computeSVD(2, True) >>> svd_model.U.rows.collect() # doctest: +NORMALIZE_WHITESPACE [IndexedRow(0, [-0.707106781187,0.707106781187]),\ IndexedRow(1, [-0.707106781187,-0.707106781187])] >>> svd_model.s DenseVector([3.4641, 3.1623]) >>> svd_model.V DenseMatrix(3, 2, [-0.4082, -0.8165, -0.4082, 0.8944, -0.4472, 0.0], 0) """ j_model = self._java_matrix_wrapper.call( "computeSVD", int(k), bool(computeU), float(rCond)) return SingularValueDecomposition(j_model)
[docs] @since('2.2.0') def multiply(self, matrix): """ Multiply this matrix by a local dense matrix on the right. :param matrix: a local dense matrix whose number of rows must match the number of columns of this matrix :returns: :py:class:`IndexedRowMatrix` >>> mat = IndexedRowMatrix(sc.parallelize([(0, (0, 1)), (1, (2, 3))])) >>> mat.multiply(DenseMatrix(2, 2, [0, 2, 1, 3])).rows.collect() [IndexedRow(0, [2.0,3.0]), IndexedRow(1, [6.0,11.0])] """ if not isinstance(matrix, DenseMatrix): raise ValueError("Only multiplication with DenseMatrix " "is supported.") return IndexedRowMatrix(self._java_matrix_wrapper.call("multiply", matrix))
[docs]class MatrixEntry(object): """ 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): """ 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 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.rdd.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] @since('2.0.0') def transpose(self): """ Transpose this CoordinateMatrix. >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2), ... MatrixEntry(1, 0, 2), ... MatrixEntry(2, 1, 3.7)]) >>> mat = CoordinateMatrix(entries) >>> mat_transposed = mat.transpose() >>> print(mat_transposed.numRows()) 2 >>> print(mat_transposed.numCols()) 3 """ java_transposed_matrix = self._java_matrix_wrapper.call("transpose") return CoordinateMatrix(java_transposed_matrix)
[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): """ 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 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.rdd.map(lambda row: ((row[0][0], row[0][1]), row[1])) return blocks @property 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 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 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 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] @since('2.0.0') def cache(self): """ Caches the underlying RDD. """ self._java_matrix_wrapper.call("cache") return self
[docs] @since('2.0.0') def persist(self, storageLevel): """ Persists the underlying RDD with the specified storage level. """ if not isinstance(storageLevel, StorageLevel): raise TypeError("`storageLevel` should be a StorageLevel, got %s" % type(storageLevel)) javaStorageLevel = self._java_matrix_wrapper._sc._getJavaStorageLevel(storageLevel) self._java_matrix_wrapper.call("persist", javaStorageLevel) return self
[docs] @since('2.0.0') def validate(self): """ Validates the block matrix info against the matrix data (`blocks`) and throws an exception if any error is found. """ self._java_matrix_wrapper.call("validate")
[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] @since('2.0.0') def subtract(self, other): """ Subtracts the given block matrix `other` from this block matrix: `this - other`. The matrices must have the same size and matching `rowsPerBlock` and `colsPerBlock` values. If one of the sub matrix blocks that are being subtracted is a SparseMatrix, the resulting sub matrix block will also be a SparseMatrix, even if it is being subtracted from a DenseMatrix. If two dense sub matrix blocks are subtracted, the output block will also be a DenseMatrix. >>> dm1 = Matrices.dense(3, 2, [3, 1, 5, 4, 6, 2]) >>> dm2 = Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]) >>> sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 1, 2], [1, 2, 3]) >>> blocks1 = sc.parallelize([((0, 0), dm1), ((1, 0), dm2)]) >>> blocks2 = sc.parallelize([((0, 0), dm2), ((1, 0), dm1)]) >>> 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.subtract(mat2).toLocalMatrix() DenseMatrix(6, 2, [-4.0, -7.0, -4.0, 4.0, 7.0, 4.0, -6.0, -5.0, -10.0, 6.0, 5.0, 10.0], 0) >>> mat2.subtract(mat3).toLocalMatrix() DenseMatrix(6, 2, [6.0, 8.0, 9.0, -4.0, -7.0, -4.0, 10.0, 9.0, 9.0, -6.0, -5.0, -10.0], 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("subtract", 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] @since('2.0.0') def transpose(self): """ Transpose this BlockMatrix. Returns a new BlockMatrix instance sharing the same underlying data. Is a lazy operation. >>> 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_transposed = mat.transpose() >>> mat_transposed.toLocalMatrix() DenseMatrix(2, 6, [1.0, 4.0, 2.0, 5.0, 3.0, 6.0, 7.0, 10.0, 8.0, 11.0, 9.0, 12.0], 0) """ java_transposed_matrix = self._java_matrix_wrapper.call("transpose") return BlockMatrix(java_transposed_matrix, self.colsPerBlock, self.rowsPerBlock)
[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 import numpy from pyspark.sql import SparkSession from pyspark.mllib.linalg import Matrices import pyspark.mllib.linalg.distributed try: # Numpy 1.14+ changed it's string format. numpy.set_printoptions(legacy='1.13') except TypeError: pass globs = pyspark.mllib.linalg.distributed.__dict__.copy() spark = SparkSession.builder\ .master("local[2]")\ .appName("mllib.linalg.distributed tests")\ .getOrCreate() globs['sc'] = spark.sparkContext globs['Matrices'] = Matrices (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()