# Data Types - RDD-based API

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 Breeze. A training example used in supervised learning is called a “labeled point” in MLlib.

## Local vector

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.

MLlib recognizes the following types as dense vectors:

- NumPy’s
`array`

- Python’s list, e.g.,
`[1, 2, 3]`

and the following as sparse vectors:

- MLlib’s
`SparseVector`

. - SciPy’s
`csc_matrix`

with a single column

We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented
in `Vectors`

to create sparse vectors.

Refer to the `Vectors`

Python docs for more details on the API.

The base class of local vectors is
`Vector`

, and we provide two
implementations: `DenseVector`

and
`SparseVector`

. We recommend
using the factory methods implemented in
`Vectors`

to create local vectors.

Refer to the `Vector`

Scala docs and `Vectors`

Scala docs for details on the API.

** 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`

.The base class of local vectors is
`Vector`

, and we provide two
implementations: `DenseVector`

and
`SparseVector`

. We recommend
using the factory methods implemented in
`Vectors`

to create local vectors.

Refer to the `Vector`

Java docs and `Vectors`

Java docs for details on the API.

## Labeled point

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, ...`

.

A labeled point is represented by
`LabeledPoint`

.

Refer to the `LabeledPoint`

Python docs for more details on the API.

A labeled point is represented by the case class
`LabeledPoint`

.

Refer to the `LabeledPoint`

Scala docs for details on the API.

A labeled point is represented by
`LabeledPoint`

.

Refer to the `LabeledPoint`

Java docs for details on the API.

*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
`LIBSVM`

and
`LIBLINEAR`

. 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.

`MLUtils.loadLibSVMFile`

reads training
examples stored in LIBSVM format.

Refer to the `MLUtils`

Python docs for more details on the API.

`MLUtils.loadLibSVMFile`

reads training
examples stored in LIBSVM format.

Refer to the `MLUtils`

Scala docs for details on the API.

`MLUtils.loadLibSVMFile`

reads training
examples stored in LIBSVM format.

Refer to the `MLUtils`

Java docs for details on the API.

## Local matrix

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)`

.

The base class of local matrices is
`Matrix`

, and we provide two
implementations: `DenseMatrix`

,
and `SparseMatrix`

.
We recommend using the factory methods implemented
in `Matrices`

to create local
matrices. Remember, local matrices in MLlib are stored in column-major order.

Refer to the `Matrix`

Python docs and `Matrices`

Python docs for more details on the API.

The base class of local matrices is
`Matrix`

, and we provide two
implementations: `DenseMatrix`

,
and `SparseMatrix`

.
We recommend using the factory methods implemented
in `Matrices`

to create local
matrices. Remember, local matrices in MLlib are stored in column-major order.

Refer to the `Matrix`

Scala docs and `Matrices`

Scala docs for details on the API.

The base class of local matrices is
`Matrix`

, and we provide two
implementations: `DenseMatrix`

,
and `SparseMatrix`

.
We recommend using the factory methods implemented
in `Matrices`

to create local
matrices. Remember, local matrices in MLlib are stored in column-major order.

Refer to the `Matrix`

Java docs and `Matrices`

Java docs for details on the API.

## Distributed matrix

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 vector.
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 coordinate list (COO) 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.

### RowMatrix

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.

A `RowMatrix`

can be
created from an `RDD`

of vectors.

Refer to the `RowMatrix`

Python docs for more details on the API.

A `RowMatrix`

can be
created from an `RDD[Vector]`

instance. Then we can compute its column summary statistics and decompositions.
QR decomposition is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
For singular value decomposition (SVD) and principal component analysis (PCA), please refer to Dimensionality reduction.

Refer to the `RowMatrix`

Scala docs for details on the API.

A `RowMatrix`

can be
created from a `JavaRDD<Vector>`

instance. Then we can compute its column summary statistics.

Refer to the `RowMatrix`

Java docs for details on the API.

### IndexedRowMatrix

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.

An `IndexedRowMatrix`

can be created from an `RDD`

of `IndexedRow`

s, where
`IndexedRow`

is a
wrapper over `(long, vector)`

. An `IndexedRowMatrix`

can be converted to a `RowMatrix`

by dropping
its row indices.

Refer to the `IndexedRowMatrix`

Python docs for more details on the API.

An
`IndexedRowMatrix`

can be created from an `RDD[IndexedRow]`

instance, where
`IndexedRow`

is a
wrapper over `(Long, Vector)`

. An `IndexedRowMatrix`

can be converted to a `RowMatrix`

by dropping
its row indices.

Refer to the `IndexedRowMatrix`

Scala docs for details on the API.

An
`IndexedRowMatrix`

can be created from a `JavaRDD<IndexedRow>`

instance, where
`IndexedRow`

is a
wrapper over `(long, Vector)`

. An `IndexedRowMatrix`

can be converted to a `RowMatrix`

by dropping
its row indices.

Refer to the `IndexedRowMatrix`

Java docs for details on the API.

### CoordinateMatrix

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.

A `CoordinateMatrix`

can be created from an `RDD`

of `MatrixEntry`

entries, where
`MatrixEntry`

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 `CoordinateMatrix`

Python docs for more details on the API.

A
`CoordinateMatrix`

can be created from an `RDD[MatrixEntry]`

instance, where
`MatrixEntry`

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 `CoordinateMatrix`

Scala docs for details on the API.

A
`CoordinateMatrix`

can be created from a `JavaRDD<MatrixEntry>`

instance, where
`MatrixEntry`

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 `CoordinateMatrix`

Java docs for details on the API.

### BlockMatrix

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.

A `BlockMatrix`

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 `BlockMatrix`

Python docs for more details on the API.

A `BlockMatrix`

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 `BlockMatrix`

Scala docs for details on the API.

A `BlockMatrix`

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 `BlockMatrix`

Java docs for details on the API.