public class RowMatrix extends Object implements DistributedMatrix, org.apache.spark.internal.Logging
param: rows rows stored as an RDD[Vector]
param: nRows number of rows. A non-positive value means unknown, and then the number of rows will
be determined by the number of records in the RDD rows
.
param: nCols number of columns. A non-positive value means unknown, and then the number of
columns will be determined by the size of the first row.
Constructor and Description |
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RowMatrix(RDD<Vector> rows)
Alternative constructor leaving matrix dimensions to be determined automatically.
|
RowMatrix(RDD<Vector> rows,
long nRows,
int nCols) |
Modifier and Type | Method and Description |
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CoordinateMatrix |
columnSimilarities()
Compute all cosine similarities between columns of this matrix using the brute-force
approach of computing normalized dot products.
|
CoordinateMatrix |
columnSimilarities(double threshold)
Compute similarities between columns of this matrix using a sampling approach.
|
MultivariateStatisticalSummary |
computeColumnSummaryStatistics()
Computes column-wise summary statistics.
|
Matrix |
computeCovariance()
Computes the covariance matrix, treating each row as an observation.
|
Matrix |
computeGramianMatrix()
Computes the Gramian matrix
A^T A . |
Matrix |
computePrincipalComponents(int k)
Computes the top k principal components only.
|
scala.Tuple2<Matrix,Vector> |
computePrincipalComponentsAndExplainedVariance(int k)
Computes the top k principal components and a vector of proportions of
variance explained by each principal component.
|
SingularValueDecomposition<RowMatrix,Matrix> |
computeSVD(int k,
boolean computeU,
double rCond)
Computes singular value decomposition of this matrix.
|
RowMatrix |
multiply(Matrix B)
Multiply this matrix by a local matrix on the right.
|
long |
numCols()
Gets or computes the number of columns.
|
long |
numRows()
Gets or computes the number of rows.
|
RDD<Vector> |
rows() |
QRDecomposition<RowMatrix,Matrix> |
tallSkinnyQR(boolean computeQ)
Compute QR decomposition for
RowMatrix . |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public long numCols()
numCols
in interface DistributedMatrix
public long numRows()
numRows
in interface DistributedMatrix
public Matrix computeGramianMatrix()
A^T A
.
public SingularValueDecomposition<RowMatrix,Matrix> computeSVD(int k, boolean computeU, double rCond)
At most k largest non-zero singular values and associated vectors are returned. If there are k such values, then the dimensions of the return will be: - U is a RowMatrix of size m x k that satisfies U' * U = eye(k), - s is a Vector of size k, holding the singular values in descending order, - V is a Matrix of size n x k that satisfies V' * V = eye(k).
We assume n is smaller than m, though this is not strictly required. The singular values and the right singular vectors are derived from the eigenvalues and the eigenvectors of the Gramian matrix A' * A. U, the matrix storing the right singular vectors, is computed via matrix multiplication as U = A * (V * S^-1^), if requested by user. The actual method to use is determined automatically based on the cost: - If n is small (n < 100) or k is large compared with n (k > n / 2), we compute the Gramian matrix first and then compute its top eigenvalues and eigenvectors locally on the driver. This requires a single pass with O(n^2^) storage on each executor and on the driver, and O(n^2^ k) time on the driver. - Otherwise, we compute (A' * A) * v in a distributive way and send it to ARPACK's DSAUPD to compute (A' * A)'s top eigenvalues and eigenvectors on the driver node. This requires O(k) passes, O(n) storage on each executor, and O(n k) storage on the driver.
Several internal parameters are set to default values. The reciprocal condition number rCond is set to 1e-9. All singular values smaller than rCond * sigma(0) are treated as zeros, where sigma(0) is the largest singular value. The maximum number of Arnoldi update iterations for ARPACK is set to 300 or k * 3, whichever is larger. The numerical tolerance for ARPACK's eigen-decomposition is set to 1e-10.
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).computeU
- whether to compute UrCond
- the reciprocal condition number. All singular values smaller than rCond * sigma(0)
are treated as zero, where sigma(0) is the largest singular value.public Matrix computeCovariance()
public scala.Tuple2<Matrix,Vector> computePrincipalComponentsAndExplainedVariance(int k)
k
- number of top principal components.public Matrix computePrincipalComponents(int k)
k
- number of top principal components.computePrincipalComponentsAndExplainedVariance
public MultivariateStatisticalSummary computeColumnSummaryStatistics()
public RowMatrix multiply(Matrix B)
B
- a local matrix whose number of rows must match the number of columns of this matrixRowMatrix
representing the product,
which preserves partitioningpublic CoordinateMatrix columnSimilarities()
public CoordinateMatrix columnSimilarities(double threshold)
The threshold parameter is a trade-off knob between estimate quality and computational cost.
Setting a threshold of 0 guarantees deterministic correct results, but comes at exactly the same cost as the brute-force approach. Setting the threshold to positive values 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.
threshold
- Set to 0 for deterministic guaranteed correctness.
Similarities above this threshold are estimated
with the cost vs estimate quality trade-off described above.public QRDecomposition<RowMatrix,Matrix> tallSkinnyQR(boolean computeQ)
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" (see here)
computeQ
- whether to computeQ