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import sys
import numpy as np
if sys.version > '3':
xrange = range
basestring = str
from pyspark import SparkContext, since
from pyspark.mllib.common import callMLlibFunc, inherit_doc
from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
from pyspark.sql import DataFrame
[docs]class MLUtils(object):
"""
Helper methods to load, save and pre-process data used in MLlib.
.. versionadded:: 1.0.0
"""
@staticmethod
def _parse_libsvm_line(line):
"""
Parses a line in LIBSVM format into (label, indices, values).
"""
items = line.split(None)
label = float(items[0])
nnz = len(items) - 1
indices = np.zeros(nnz, dtype=np.int32)
values = np.zeros(nnz)
for i in xrange(nnz):
index, value = items[1 + i].split(":")
indices[i] = int(index) - 1
values[i] = float(value)
return label, indices, values
@staticmethod
def _convert_labeled_point_to_libsvm(p):
"""Converts a LabeledPoint to a string in LIBSVM format."""
from pyspark.mllib.regression import LabeledPoint
assert isinstance(p, LabeledPoint)
items = [str(p.label)]
v = _convert_to_vector(p.features)
if isinstance(v, SparseVector):
nnz = len(v.indices)
for i in xrange(nnz):
items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
else:
for i in xrange(len(v)):
items.append(str(i + 1) + ":" + str(v[i]))
return " ".join(items)
[docs] @staticmethod
@since("1.0.0")
def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None):
"""
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.
:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param numFeatures: 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.
:param minPartitions: min number of partitions
:return: labeled data stored as an RDD of LabeledPoint
>>> 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]))
"""
from pyspark.mllib.regression import LabeledPoint
lines = sc.textFile(path, minPartitions)
parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l))
if numFeatures <= 0:
parsed.cache()
numFeatures = parsed.map(lambda x: -1 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2])))
[docs] @staticmethod
@since("1.0.0")
def saveAsLibSVMFile(data, dir):
"""
Save labeled data in LIBSVM format.
:param data: an RDD of LabeledPoint to be saved
:param dir: directory to save the data
>>> 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'
"""
lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
lines.saveAsTextFile(dir)
[docs] @staticmethod
@since("1.1.0")
def loadLabeledPoints(sc, path, minPartitions=None):
"""
Load labeled points saved using RDD.saveAsTextFile.
:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param minPartitions: min number of partitions
:return: labeled data stored as an RDD of LabeledPoint
>>> 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])]
"""
minPartitions = minPartitions or min(sc.defaultParallelism, 2)
return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
[docs] @staticmethod
@since("1.5.0")
def appendBias(data):
"""
Returns a new vector with `1.0` (bias) appended to
the end of the input vector.
"""
vec = _convert_to_vector(data)
if isinstance(vec, SparseVector):
newIndices = np.append(vec.indices, len(vec))
newValues = np.append(vec.values, 1.0)
return SparseVector(len(vec) + 1, newIndices, newValues)
else:
return _convert_to_vector(np.append(vec.toArray(), 1.0))
[docs] @staticmethod
@since("1.5.0")
def loadVectors(sc, path):
"""
Loads vectors saved using `RDD[Vector].saveAsTextFile`
with the default number of partitions.
"""
return callMLlibFunc("loadVectors", sc, path)
[docs] @staticmethod
@since("2.0.0")
def convertVectorColumnsToML(dataset, *cols):
"""
Converts vector columns in an input DataFrame from the
:py:class:`pyspark.mllib.linalg.Vector` type to the new
:py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`
package.
:param dataset:
input dataset
:param cols:
a list of vector columns to be converted.
New vector columns will be ignored. If unspecified, all old
vector columns will be converted excepted nested ones.
:return:
the input dataset with old vector columns converted to the
new vector type
>>> 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
"""
if not isinstance(dataset, DataFrame):
raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
return callMLlibFunc("convertVectorColumnsToML", dataset, list(cols))
[docs] @staticmethod
@since("2.0.0")
def convertVectorColumnsFromML(dataset, *cols):
"""
Converts vector columns in an input DataFrame to the
:py:class:`pyspark.mllib.linalg.Vector` type from the new
:py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`
package.
:param dataset:
input dataset
:param cols:
a list of vector columns to be converted.
Old vector columns will be ignored. If unspecified, all new
vector columns will be converted except nested ones.
:return:
the input dataset with new vector columns converted to the
old vector type
>>> 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
"""
if not isinstance(dataset, DataFrame):
raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
return callMLlibFunc("convertVectorColumnsFromML", dataset, list(cols))
[docs] @staticmethod
@since("2.0.0")
def convertMatrixColumnsToML(dataset, *cols):
"""
Converts matrix columns in an input DataFrame from the
:py:class:`pyspark.mllib.linalg.Matrix` type to the new
:py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`
package.
:param dataset:
input dataset
:param cols:
a list of matrix columns to be converted.
New matrix columns will be ignored. If unspecified, all old
matrix columns will be converted excepted nested ones.
:return:
the input dataset with old matrix columns converted to the
new matrix type
>>> 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
"""
if not isinstance(dataset, DataFrame):
raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
return callMLlibFunc("convertMatrixColumnsToML", dataset, list(cols))
[docs] @staticmethod
@since("2.0.0")
def convertMatrixColumnsFromML(dataset, *cols):
"""
Converts matrix columns in an input DataFrame to the
:py:class:`pyspark.mllib.linalg.Matrix` type from the new
:py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`
package.
:param dataset:
input dataset
:param cols:
a list of matrix columns to be converted.
Old matrix columns will be ignored. If unspecified, all new
matrix columns will be converted except nested ones.
:return:
the input dataset with new matrix columns converted to the
old matrix type
>>> 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
"""
if not isinstance(dataset, DataFrame):
raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
return callMLlibFunc("convertMatrixColumnsFromML", dataset, list(cols))
[docs]class Saveable(object):
"""
Mixin for models and transformers which may be saved as files.
.. versionadded:: 1.3.0
"""
[docs] def save(self, sc, path):
"""
Save this model to the given path.
This saves:
* human-readable (JSON) model metadata to path/metadata/
* Parquet formatted data to path/data/
The model may be loaded using :py:meth:`Loader.load`.
:param sc: Spark context used to save model data.
:param path: Path specifying the directory in which to save
this model. If the directory already exists,
this method throws an exception.
"""
raise NotImplementedError
[docs]@inherit_doc
class JavaSaveable(Saveable):
"""
Mixin for models that provide save() through their Scala
implementation.
.. versionadded:: 1.3.0
"""
[docs] @since("1.3.0")
def save(self, sc, path):
"""Save this model to the given path."""
if not isinstance(sc, SparkContext):
raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
if not isinstance(path, basestring):
raise TypeError("path should be a basestring, got type %s" % type(path))
self._java_model.save(sc._jsc.sc(), path)
[docs]class Loader(object):
"""
Mixin for classes which can load saved models from files.
.. versionadded:: 1.3.0
"""
[docs] @classmethod
def load(cls, sc, path):
"""
Load a model from the given path. The model should have been
saved using :py:meth:`Saveable.save`.
:param sc: Spark context used for loading model files.
:param path: Path specifying the directory to which the model
was saved.
:return: model instance
"""
raise NotImplementedError
[docs]@inherit_doc
class JavaLoader(Loader):
"""
Mixin for classes which can load saved models using its Scala
implementation.
.. versionadded:: 1.3.0
"""
@classmethod
def _java_loader_class(cls):
"""
Returns the full class name of the Java loader. The default
implementation replaces "pyspark" by "org.apache.spark" in
the Python full class name.
"""
java_package = cls.__module__.replace("pyspark", "org.apache.spark")
return ".".join([java_package, cls.__name__])
@classmethod
def _load_java(cls, sc, path):
"""
Load a Java model from the given path.
"""
java_class = cls._java_loader_class()
java_obj = sc._jvm
for name in java_class.split("."):
java_obj = getattr(java_obj, name)
return java_obj.load(sc._jsc.sc(), path)
[docs] @classmethod
@since("1.3.0")
def load(cls, sc, path):
"""Load a model from the given path."""
java_model = cls._load_java(sc, path)
return cls(java_model)
[docs]class LinearDataGenerator(object):
"""Utils for generating linear data.
.. versionadded:: 1.5.0
"""
[docs] @staticmethod
@since("1.5.0")
def generateLinearRDD(sc, nexamples, nfeatures, eps,
nParts=2, intercept=0.0):
"""
Generate an RDD of LabeledPoints.
"""
return callMLlibFunc(
"generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures),
float(eps), int(nParts), float(intercept))
def _test():
import doctest
from pyspark.sql import SparkSession
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
spark = SparkSession.builder\
.master("local[2]")\
.appName("mllib.util tests")\
.getOrCreate()
globs['spark'] = spark
globs['sc'] = spark.sparkContext
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()
