MLUtils¶

class pyspark.mllib.util.MLUtils[source]¶

Helper methods to load, save and pre-process data used in MLlib.

New in version 1.0.0.

Methods

`appendBias`(data)	Returns a new vector with 1.0 (bias) appended to the end of the input vector.
`convertMatrixColumnsFromML`(dataset, *cols)	Converts matrix columns in an input DataFrame to the `pyspark.mllib.linalg.Matrix` type from the new `pyspark.ml.linalg.Matrix` type under the spark.ml package.
`convertMatrixColumnsToML`(dataset, *cols)	Converts matrix columns in an input DataFrame from the `pyspark.mllib.linalg.Matrix` type to the new `pyspark.ml.linalg.Matrix` type under the spark.ml package.
`convertVectorColumnsFromML`(dataset, *cols)	Converts vector columns in an input DataFrame to the `pyspark.mllib.linalg.Vector` type from the new `pyspark.ml.linalg.Vector` type under the spark.ml package.
`convertVectorColumnsToML`(dataset, *cols)	Converts vector columns in an input DataFrame from the `pyspark.mllib.linalg.Vector` type to the new `pyspark.ml.linalg.Vector` type under the spark.ml package.
`loadLabeledPoints`(sc, path[, minPartitions])	Load labeled points saved using RDD.saveAsTextFile.
`loadLibSVMFile`(sc, path[, numFeatures, …])	Loads labeled data in the LIBSVM format into an RDD of LabeledPoint.
`loadVectors`(sc, path)	Loads vectors saved using RDD[Vector].saveAsTextFile with the default number of partitions.
`saveAsLibSVMFile`(data, dir)	Save labeled data in LIBSVM format.

Methods Documentation

static appendBias(data)[source]¶: Returns a new vector with 1.0 (bias) appended to the end of the input vector.

New in version 1.5.0.

static convertMatrixColumnsFromML(dataset, *cols)[source]¶

Converts matrix columns in an input DataFrame to the pyspark.mllib.linalg.Matrix type from the new pyspark.ml.linalg.Matrix type under the spark.ml package.

New in version 2.0.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

*colsstr

Matrix columns to be converted.

Old matrix columns will be ignored. If unspecified, all new matrix columns will be converted except nested ones.

Returns:

pyspark.sql.DataFrame: the input dataset with new matrix columns converted to the old matrix type

Examples

>>> import pyspark
>>> from pyspark.ml.linalg import Matrices
>>> from pyspark.mllib.util import MLUtils
>>> df = spark.createDataFrame(
...     [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]),
...     Matrices.dense(2, 2, range(4)))], ["id", "x", "y"])
>>> r1 = MLUtils.convertMatrixColumnsFromML(df).first()
>>> isinstance(r1.x, pyspark.mllib.linalg.SparseMatrix)
True
>>> isinstance(r1.y, pyspark.mllib.linalg.DenseMatrix)
True
>>> r2 = MLUtils.convertMatrixColumnsFromML(df, "x").first()
>>> isinstance(r2.x, pyspark.mllib.linalg.SparseMatrix)
True
>>> isinstance(r2.y, pyspark.ml.linalg.DenseMatrix)
True

static convertMatrixColumnsToML(dataset, *cols)[source]¶

Converts matrix columns in an input DataFrame from the pyspark.mllib.linalg.Matrix type to the new pyspark.ml.linalg.Matrix type under the spark.ml package.

New in version 2.0.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

*colsstr

Matrix columns to be converted.

New matrix columns will be ignored. If unspecified, all old matrix columns will be converted excepted nested ones.

Returns:

pyspark.sql.DataFrame: the input dataset with old matrix columns converted to the new matrix type

Examples

>>> import pyspark
>>> from pyspark.mllib.linalg import Matrices
>>> from pyspark.mllib.util import MLUtils
>>> df = spark.createDataFrame(
...     [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]),
...     Matrices.dense(2, 2, range(4)))], ["id", "x", "y"])
>>> r1 = MLUtils.convertMatrixColumnsToML(df).first()
>>> isinstance(r1.x, pyspark.ml.linalg.SparseMatrix)
True
>>> isinstance(r1.y, pyspark.ml.linalg.DenseMatrix)
True
>>> r2 = MLUtils.convertMatrixColumnsToML(df, "x").first()
>>> isinstance(r2.x, pyspark.ml.linalg.SparseMatrix)
True
>>> isinstance(r2.y, pyspark.mllib.linalg.DenseMatrix)
True

static convertVectorColumnsFromML(dataset, *cols)[source]¶

Converts vector columns in an input DataFrame to the pyspark.mllib.linalg.Vector type from the new pyspark.ml.linalg.Vector type under the spark.ml package.

New in version 2.0.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

*colsstr

Vector columns to be converted.

Old vector columns will be ignored. If unspecified, all new vector columns will be converted except nested ones.

Returns:

pyspark.sql.DataFrame: the input dataset with new vector columns converted to the old vector type

Examples

>>> import pyspark
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.mllib.util import MLUtils
>>> df = spark.createDataFrame(
...     [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],
...     ["id", "x", "y"])
>>> r1 = MLUtils.convertVectorColumnsFromML(df).first()
>>> isinstance(r1.x, pyspark.mllib.linalg.SparseVector)
True
>>> isinstance(r1.y, pyspark.mllib.linalg.DenseVector)
True
>>> r2 = MLUtils.convertVectorColumnsFromML(df, "x").first()
>>> isinstance(r2.x, pyspark.mllib.linalg.SparseVector)
True
>>> isinstance(r2.y, pyspark.ml.linalg.DenseVector)
True

static convertVectorColumnsToML(dataset, *cols)[source]¶

Converts vector columns in an input DataFrame from the pyspark.mllib.linalg.Vector type to the new pyspark.ml.linalg.Vector type under the spark.ml package.

New in version 2.0.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

*colsstr

Vector columns to be converted.

New vector columns will be ignored. If unspecified, all old vector columns will be converted excepted nested ones.

Returns:

pyspark.sql.DataFrame: the input dataset with old vector columns converted to the new vector type

Examples

>>> import pyspark
>>> from pyspark.mllib.linalg import Vectors
>>> from pyspark.mllib.util import MLUtils
>>> df = spark.createDataFrame(
...     [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],
...     ["id", "x", "y"])
>>> r1 = MLUtils.convertVectorColumnsToML(df).first()
>>> isinstance(r1.x, pyspark.ml.linalg.SparseVector)
True
>>> isinstance(r1.y, pyspark.ml.linalg.DenseVector)
True
>>> r2 = MLUtils.convertVectorColumnsToML(df, "x").first()
>>> isinstance(r2.x, pyspark.ml.linalg.SparseVector)
True
>>> isinstance(r2.y, pyspark.mllib.linalg.DenseVector)
True

static loadLabeledPoints(sc, path, minPartitions=None)[source]¶

Load labeled points saved using RDD.saveAsTextFile.

New in version 1.0.0.

Parameters:

scpyspark.SparkContext: Spark context
pathstr: file or directory path in any Hadoop-supported file system URI
minPartitionsint, optional: min number of partitions

Returns:

pyspark.RDD: labeled data stored as an RDD of LabeledPoint

Examples

>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> from pyspark.mllib.regression import LabeledPoint
>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])),
...             LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)
>>> MLUtils.loadLabeledPoints(sc, tempFile.name).collect()
[LabeledPoint(1.1, (3,[0,2],[-1.23,4.56e-07])), LabeledPoint(0.0, [1.01,2.02,3.03])]

static loadLibSVMFile(sc, path, numFeatures=- 1, minPartitions=None)[source]¶

Loads labeled data in the LIBSVM format into an RDD of LabeledPoint. The LIBSVM format is a text-based format used by LIBSVM and LIBLINEAR. 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. This method parses each line into a LabeledPoint, where the feature indices are converted to zero-based.

New in version 1.0.0.

Parameters:

scpyspark.SparkContext: Spark context
pathstr: file or directory path in any Hadoop-supported file system URI
numFeaturesint, optional: number of features, which will be determined from the input data if a nonpositive value is given. This is useful when the dataset is already split into multiple files and you want to load them separately, because some features may not present in certain files, which leads to inconsistent feature dimensions.
minPartitionsint, optional: min number of partitions

Returns:

pyspark.RDD: labeled data stored as an RDD of LabeledPoint

Examples

>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> from pyspark.mllib.regression import LabeledPoint
>>> tempFile = NamedTemporaryFile(delete=True)
>>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\n-1\n-1 2:4.0 4:5.0 6:6.0")
>>> tempFile.flush()
>>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
>>> tempFile.close()
>>> examples[0]
LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0]))
>>> examples[1]
LabeledPoint(-1.0, (6,[],[]))
>>> examples[2]
LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0]))

static loadVectors(sc, path)[source]¶: Loads vectors saved using RDD[Vector].saveAsTextFile with the default number of partitions.

New in version 1.5.0.

static saveAsLibSVMFile(data, dir)[source]¶

Save labeled data in LIBSVM format.

New in version 1.0.0.

Parameters:

datapyspark.RDD: an RDD of LabeledPoint to be saved
dirstr: directory to save the data

Examples

>>> from tempfile import NamedTemporaryFile
>>> from fileinput import input
>>> from pyspark.mllib.regression import LabeledPoint
>>> from glob import glob
>>> from pyspark.mllib.util import MLUtils
>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),
...             LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
>>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
'0.0 1:1.01 2:2.02 3:3.03\n1.1 1:1.23 3:4.56\n'

Loader Saveable