Project: Apache Spark (App Definition and Development / Streaming & Messaging)
File: mllib-data-types.md

layout: global title: Data Types - RDD-based API displayTitle: Data Types - RDD-based API license: | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Table of contents {:toc} MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. Local vectors and local matrices are simple data models that serve as public interfaces. The underlying linear algebra operations are provided by . A training example used in supervised learning is called a "labeled point" in MLlib. A local vector has integer-typed and 0-based indices and double-typed values, stored on a single machine. MLlib supports two types of local vectors: dense and sparse. A dense vector is backed by a double array representing its entry values, while a sparse vector is backed by two parallel arrays: indices and values. For example, a vector `(1.0, 0.0, 3.0)` can be represented in dense format as `[1.0, 0.0, 3.0]` or in sparse format as `(3, [0, 2], [1.0, 3.0])`, where `3` is the size of the vector. <div class="codetabs"> <div data-lang="python" markdown="1"> MLlib recognizes the following types as dense vectors: NumPy's Python's list, e.g., `[1, 2, 3]` and the following as sparse vectors: MLlib's . SciPy's with a single column We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented in to create sparse vectors. Refer to the for more details on the API. {% highlight python %} import numpy as np import scipy.sparse as sps from pyspark.mllib.linalg import Vectors dv1 = np.array([1.0, 0.0, 3.0]) dv2 = [1.0, 0.0, 3.0] sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0]) sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape=(3, 1)) {% endhighlight %} </div> <div data-lang="scala" markdown="1"> The base class of local vectors is , and we provide two implementations: and . We recommend using the factory methods implemented in to create local vectors. Refer to the and for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.{Vector, Vectors} // Create a dense vector (1.0, 0.0, 3.0). val dv: Vector = Vectors.dense(1.0, 0.0, 3.0) // Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries. val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)) // Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries. val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0))) {% endhighlight %} *Note:* Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`. </div> <div data-lang="java" markdown="1"> The base class of local vectors is , and we provide two implementations: and

We recommend using the factory methods implemented in to create local vectors. Refer to the and for details on the API. {% highlight java %} import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; // Create a dense vector (1.0, 0.0, 3.0). Vector dv = Vectors.dense(1.0, 0.0, 3.0); // Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries. Vector sv = Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0}); {% endhighlight %} </div> </div> A labeled point is a local vector, either dense or sparse, associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms. We use a double to store a label, so we can use labeled points in both regression and classification. For binary classification, a label should be either `0` (negative) or `1` (positive). For multiclass classification, labels should be class indices starting from zero: `0, 1, 2, ...`. <div class="codetabs"> <div data-lang="python" markdown="1"> A labeled point is represented by . Refer to the for more details on the API. {% highlight python %} from pyspark.mllib.linalg import SparseVector from pyspark.mllib.regression import LabeledPoint pos = LabeledPoint(1.0, [1.0, 0.0, 3.0]) neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0])) {% endhighlight %} </div> <div data-lang="scala" markdown="1"> A labeled point is represented by the case class . Refer to the for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint // Create a labeled point with a positive label and a dense feature vector. val pos = LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)) // Create a labeled point with a negative label and a sparse feature vector. val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))) {% endhighlight %} </div> <div data-lang="java" markdown="1"> A labeled point is represented by . Refer to the for details on the API. {% highlight java %} import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; // Create a labeled point with a positive label and a dense feature vector. LabeledPoint pos = new LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)); // Create a labeled point with a negative label and a sparse feature vector. LabeledPoint neg = new LabeledPoint(0.0, Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0})); {% endhighlight %} </div> </div> *Sparse data* It is very common in practice to have sparse training data. MLlib supports reading training examples stored in `LIBSVM` format, which is the default format used by and . It is a text format in which each line represents a labeled sparse feature vector using the following format: ~~~ label index1:value1 index2:value2 ... ~~~ where the indices are one-based and in ascending order. After loading, the feature indices are converted to zero-based. <div class="codetabs"> <div data-lang="python" markdown="1"> reads training examples stored in LIBSVM format. Refer to the for more details on the API. {% highlight python %} from pyspark.mllib.util import MLUtils examples = MLUtils.loadLibSVMFile(sc, "data/mllib/samplelibsvmdata.txt") {% endhighlight %} </div> <div data-lang="scala" markdown="1"> reads training examples stored in LIBSVM format. Refer to the for details on the API. {% highlight scala %} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/samplelibsvmdata.txt") {% endhighlight %} </div> <div data-lang="java" markdown="1"> reads training examples stored in LIBSVM format. Refer to the for details on the API. {% highlight java %} import org.apache.spark.mllib.regression.LabeledPoint; import

import org.apache.spark.api.java.JavaRDD; JavaRDD<LabeledPoint> examples = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/samplelibsvmdata.txt").toJavaRDD(); {% endhighlight %} </div> </div> A local matrix has integer-typed row and column indices and double-typed values, stored on a single machine. MLlib supports dense matrices, whose entry values are stored in a single double array in column-major order, and sparse matrices, whose non-zero entry values are stored in the Compressed Sparse Column (CSC) format in column-major order. For example, the following dense matrix `\[ \begin{pmatrix} 1.0 & 2.0 \\ 3.0 & 4.0 \\ 5.0 & 6.0 \end{pmatrix} \]` is stored in a one-dimensional array `[1.0, 3.0, 5.0, 2.0, 4.0, 6.0]` with the matrix size `(3, 2)`. <div class="codetabs"> <div data-lang="python" markdown="1"> The base class of local matrices is , and we provide two implementations: , and . We recommend using the factory methods implemented in to create local matrices. Remember, local matrices in MLlib are stored in column-major order. Refer to the and for more details on the API. {% highlight python %} from pyspark.mllib.linalg import Matrix, Matrices dm2 = Matrices.dense(3, 2, [1, 3, 5, 2, 4, 6]) sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8]) {% endhighlight %} </div> <div data-lang="scala" markdown="1"> The base class of local matrices is , and we provide two implementations: , and . We recommend using the factory methods implemented in to create local matrices. Remember, local matrices in MLlib are stored in column-major order. Refer to the and for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.{Matrix, Matrices} // Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0)) val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0)) // Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0)) val sm: Matrix = Matrices.sparse(3, 2, Array(0, 1, 3), Array(0, 2, 1), Array(9, 6, 8)) {% endhighlight %} </div> <div data-lang="java" markdown="1"> The base class of local matrices is , and we provide two implementations: , and . We recommend using the factory methods implemented in to create local matrices. Remember, local matrices in MLlib are stored in column-major order. Refer to the and for details on the API. {% highlight java %} import org.apache.spark.mllib.linalg.Matrix; import org.apache.spark.mllib.linalg.Matrices; // Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0)) Matrix dm = Matrices.dense(3, 2, new double[] {1.0, 3.0, 5.0, 2.0, 4.0, 6.0}); // Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0)) Matrix sm = Matrices.sparse(3, 2, new int[] {0, 1, 3}, new int[] {0, 2, 1}, new double[] {9, 6, 8}); {% endhighlight %} </div> </div> A distributed matrix has long-typed row and column indices and double-typed values, stored distributively in one or more RDDs. It is very important to choose the right format to store large and distributed matrices. Converting a distributed matrix to a different format may require a global shuffle, which is quite expensive. Four types of distributed matrices have been implemented so far. The basic type is called `RowMatrix`. A `RowMatrix` is a row-oriented distributed matrix without meaningful row indices, e.g., a collection of feature vectors. It is backed by an RDD of its rows, where each row is a local

We assume that the number of columns is not huge for a `RowMatrix` so that a single local vector can be reasonably communicated to the driver and can also be stored / operated on using a single node. An `IndexedRowMatrix` is similar to a `RowMatrix` but with row indices, which can be used for identifying rows and executing joins. A `CoordinateMatrix` is a distributed matrix stored in format, backed by an RDD of its entries. A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock` which is a tuple of `(Int, Int, Matrix)`. *Note* The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. In general, the use of non-deterministic RDDs can lead to errors. A `RowMatrix` is a row-oriented distributed matrix without meaningful row indices, backed by an RDD of its rows, where each row is a local vector. Since each row is represented by a local vector, the number of columns is limited by the integer range but it should be much smaller in practice. <div class="codetabs"> <div data-lang="python" markdown="1"> A can be created from an `RDD` of vectors. Refer to the for more details on the API. {% highlight python %} from pyspark.mllib.linalg.distributed import RowMatrix rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) mat = RowMatrix(rows) m = mat.numRows() # 4 n = mat.numCols() # 3 rowsRDD = mat.rows {% endhighlight %} </div> <div data-lang="scala" markdown="1"> A can be created from an `RDD[Vector]` instance. Then we can compute its column summary statistics and decompositions. is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix. For and , please refer to . Refer to the for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.linalg.distributed.RowMatrix val rows: RDD[Vector] = ... // an RDD of local vectors // Create a RowMatrix from an RDD[Vector]. val mat: RowMatrix = new RowMatrix(rows) // Get its size. val m = mat.numRows() val n = mat.numCols() // QR decomposition val qrResult = mat.tallSkinnyQR(true) {% endhighlight %} </div> <div data-lang="java" markdown="1"> A can be created from a `JavaRDD<Vector>` instance. Then we can compute its column summary statistics. Refer to the for details on the API. {% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.distributed.RowMatrix; JavaRDD<Vector> rows = ... // a JavaRDD of local vectors // Create a RowMatrix from a JavaRDD<Vector>. RowMatrix mat = new RowMatrix(rows.rdd()); // Get its size. long m = mat.numRows(); long n = mat.numCols(); // QR decomposition QRDecomposition<RowMatrix, Matrix> result = mat.tallSkinnyQR(true); {% endhighlight %} </div> </div> An `IndexedRowMatrix` is similar to a `RowMatrix` but with meaningful row indices. It is backed by an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector. <div class="codetabs"> <div data-lang="python" markdown="1"> An can be created from an `RDD` of `IndexedRow`s, where is a wrapper over `(long, vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. Refer to the for more details on the API. {% highlight python %} from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]), IndexedRow(1, [4, 5, 6]), IndexedRow(2, [7, 8, 9]), IndexedRow(3, [10, 11, 12])]) indexedRows =

[1, 2, 3]), (1, [4, 5, 6]), (2, [7, 8, 9]), (3, [10, 11, 12])]) mat = IndexedRowMatrix(indexedRows) m = mat.numRows() # 4 n = mat.numCols() # 3 rowsRDD = mat.rows rowMat = mat.toRowMatrix() {% endhighlight %} </div> <div data-lang="scala" markdown="1"> An can be created from an `RDD[IndexedRow]` instance, where is a wrapper over `(Long, Vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. Refer to the for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix} val rows: RDD[IndexedRow] = ... // an RDD of indexed rows // Create an IndexedRowMatrix from an RDD[IndexedRow]. val mat: IndexedRowMatrix = new IndexedRowMatrix(rows) // Get its size. val m = mat.numRows() val n = mat.numCols() // Drop its row indices. val rowMat: RowMatrix = mat.toRowMatrix() {% endhighlight %} </div> <div data-lang="java" markdown="1"> An can be created from a `JavaRDD<IndexedRow>` instance, where is a wrapper over `(long, Vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. Refer to the for details on the API. {% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.IndexedRow; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; import org.apache.spark.mllib.linalg.distributed.RowMatrix; JavaRDD<IndexedRow> rows = ... // a JavaRDD of indexed rows // Create an IndexedRowMatrix from a JavaRDD<IndexedRow>. IndexedRowMatrix mat = new IndexedRowMatrix(rows.rdd()); // Get its size. long m = mat.numRows(); long n = mat.numCols(); // Drop its row indices. RowMatrix rowMat = mat.toRowMatrix(); {% endhighlight %} </div> </div> A `CoordinateMatrix` is a distributed matrix backed by an RDD of its entries. Each entry is a tuple of `(i: Long, j: Long, value: Double)`, where `i` is the row index, `j` is the column index, and `value` is the entry value. A `CoordinateMatrix` should be used only when both dimensions of the matrix are huge and the matrix is very sparse. <div class="codetabs"> <div data-lang="python" markdown="1"> A can be created from an `RDD` of `MatrixEntry` entries, where is a wrapper over `(long, long, float)`. A `CoordinateMatrix` can be converted to a `RowMatrix` by calling `toRowMatrix`, or to an `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. Refer to the for more details on the API. {% highlight python %} from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(2, 1, 3.7)]) entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)]) mat = CoordinateMatrix(entries) m = mat.numRows() # 3 n = mat.numCols() # 2 entriesRDD = mat.entries rowMat = mat.toRowMatrix() indexedRowMat = mat.toIndexedRowMatrix() blockMat = mat.toBlockMatrix() {% endhighlight %} </div> <div data-lang="scala" markdown="1"> A can be created from an `RDD[MatrixEntry]` instance, where is a wrapper over `(Long, Long, Double)`. A `CoordinateMatrix` can be converted to an `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. Other computations for `CoordinateMatrix` are not currently supported. Refer to the for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry} val entries: RDD[MatrixEntry] = ... // an RDD of matrix entries // Create a CoordinateMatrix from an RDD[MatrixEntry]. val mat: CoordinateMatrix = new CoordinateMatrix(entries) // Get its size. val m = mat.numRows() val n = mat.numCols() // Convert it to an IndexRowMatrix whose rows are sparse vectors. val indexedRowMatrix = mat.toIndexedRowMatrix() {% endhighlight %} </div> <div data-lang="java" markdown="1"> A can be created from a `JavaRDD<MatrixEntry>` instance, where is a wrapper over `(long, long,

A `CoordinateMatrix` can be converted to an `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. Other computations for `CoordinateMatrix` are not currently supported. Refer to the for details on the API. {% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; import org.apache.spark.mllib.linalg.distributed.MatrixEntry; JavaRDD<MatrixEntry> entries = ... // a JavaRDD of matrix entries // Create a CoordinateMatrix from a JavaRDD<MatrixEntry>. CoordinateMatrix mat = new CoordinateMatrix(entries.rdd()); // Get its size. long m = mat.numRows(); long n = mat.numCols(); // Convert it to an IndexRowMatrix whose rows are sparse vectors. IndexedRowMatrix indexedRowMatrix = mat.toIndexedRowMatrix(); {% endhighlight %} </div> </div> A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock`s, where a `MatrixBlock` is a tuple of `((Int, Int), Matrix)`, where the `(Int, Int)` is the index of the block, and `Matrix` is the sub-matrix at the given index with size `rowsPerBlock` x `colsPerBlock`. `BlockMatrix` supports methods such as `add` and `multiply` with another `BlockMatrix`. `BlockMatrix` also has a helper function `validate` which can be used to check whether the `BlockMatrix` is set up properly. <div class="codetabs"> <div data-lang="python" markdown="1"> A can be created from an `RDD` of sub-matrix blocks, where a sub-matrix block is a `((blockRowIndex, blockColIndex), sub-matrix)` tuple. Refer to the for more details on the API. {% highlight python %} from pyspark.mllib.linalg import Matrices from pyspark.mllib.linalg.distributed import 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) m = mat.numRows() # 6 n = mat.numCols() # 2 blocksRDD = mat.blocks localMat = mat.toLocalMatrix() indexedRowMat = mat.toIndexedRowMatrix() coordinateMat = mat.toCoordinateMatrix() {% endhighlight %} </div> <div data-lang="scala" markdown="1"> A can be most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. `toBlockMatrix` creates blocks of size 1024 x 1024 by default. Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. Refer to the for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry} val entries: RDD[MatrixEntry] = ... // an RDD of (i, j, v) matrix entries // Create a CoordinateMatrix from an RDD[MatrixEntry]. val coordMat: CoordinateMatrix = new CoordinateMatrix(entries) // Transform the CoordinateMatrix to a BlockMatrix val matA: BlockMatrix = coordMat.toBlockMatrix().cache() // Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. // Nothing happens if it is valid. matA.validate() // Calculate A^T A. val ata = matA.transpose.multiply(matA) {% endhighlight %} </div> <div data-lang="java" markdown="1"> A can be most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. `toBlockMatrix` creates blocks of size 1024 x 1024 by default. Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. Refer to the for details on the API. {% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.BlockMatrix; import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; JavaRDD<MatrixEntry> entries = ... // a JavaRDD of (i, j, v) Matrix Entries // Create a CoordinateMatrix from a JavaRDD<MatrixEntry>. CoordinateMatrix coordMat = new CoordinateMatrix(entries.rdd()); // Transform the CoordinateMatrix to a BlockMatrix BlockMatrix matA = coordMat.toBlockMatrix().cache(); // Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. // Nothing happens if it is valid. matA.validate(); // Calculate A^T A. BlockMatrix ata = matA.transpose().multiply(matA); {% endhighlight %} </div> </div>