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import sys
from functools import reduce
import numpy as np
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
from typing import Generic, Iterable, List, Optional, Tuple, Type, TypeVar, cast, TYPE_CHECKING
from pyspark.core.context import SparkContext
from pyspark.mllib.linalg import Vector
from pyspark.core.rdd import RDD
from pyspark.sql.dataframe import DataFrame
T = TypeVar("T")
L = TypeVar("L", bound="Loader")
JL = TypeVar("JL", bound="JavaLoader")
if TYPE_CHECKING:
from pyspark.mllib._typing import VectorLike
from py4j.java_gateway import JavaObject
from pyspark.mllib.regression import LabeledPoint
[docs]class MLUtils:
"""
Helper methods to load, save and pre-process data used in MLlib.
.. versionadded:: 1.0.0
"""
@staticmethod
def _parse_libsvm_line(line: str) -> Tuple[float, np.ndarray, np.ndarray]:
"""
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 range(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: "LabeledPoint") -> str:
"""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 range(nnz):
items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
else:
for i in range(len(v)):
items.append(str(i + 1) + ":" + str(v[i])) # type: ignore[index]
return " ".join(items)
[docs] @staticmethod
def loadLibSVMFile(
sc: SparkContext, path: str, numFeatures: int = -1, minPartitions: Optional[int] = None
) -> RDD["LabeledPoint"]:
"""
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.
.. versionadded:: 1.0.0
Parameters
----------
sc : :py:class:`pyspark.SparkContext`
Spark context
path : str
file or directory path in any Hadoop-supported file system URI
numFeatures : int, 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.
minPartitions : int, optional
min number of partitions
Returns
-------
:py:class:`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]))
"""
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]) # type: ignore[arg-type]
)
)
[docs] @staticmethod
def saveAsLibSVMFile(data: RDD["LabeledPoint"], dir: str) -> None:
"""
Save labeled data in LIBSVM format.
.. versionadded:: 1.0.0
Parameters
----------
data : :py:class:`pyspark.RDD`
an RDD of LabeledPoint to be saved
dir : str
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'
"""
lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
lines.saveAsTextFile(dir)
[docs] @staticmethod
def loadLabeledPoints(
sc: SparkContext, path: str, minPartitions: Optional[int] = None
) -> RDD["LabeledPoint"]:
"""
Load labeled points saved using RDD.saveAsTextFile.
.. versionadded:: 1.0.0
Parameters
----------
sc : :py:class:`pyspark.SparkContext`
Spark context
path : str
file or directory path in any Hadoop-supported file system URI
minPartitions : int, optional
min number of partitions
Returns
-------
:py:class:`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])]
"""
minPartitions = minPartitions or min(sc.defaultParallelism, 2)
return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
[docs] @staticmethod
@since("1.5.0")
def appendBias(data: Vector) -> Vector:
"""
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: SparkContext, path: str) -> RDD[Vector]:
"""
Loads vectors saved using `RDD[Vector].saveAsTextFile`
with the default number of partitions.
"""
return callMLlibFunc("loadVectors", sc, path)
[docs] @staticmethod
def convertVectorColumnsToML(dataset: DataFrame, *cols: str) -> DataFrame:
"""
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.
.. versionadded:: 2.0.0
Parameters
----------
dataset : :py:class:`pyspark.sql.DataFrame`
input dataset
\\*cols : str
Vector columns to be converted.
New vector columns will be ignored. If unspecified, all old
vector columns will be converted excepted nested ones.
Returns
-------
:py:class:`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
"""
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
def convertVectorColumnsFromML(dataset: DataFrame, *cols: str) -> DataFrame:
"""
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.
.. versionadded:: 2.0.0
Parameters
----------
dataset : :py:class:`pyspark.sql.DataFrame`
input dataset
\\*cols : str
Vector columns to be converted.
Old vector columns will be ignored. If unspecified, all new
vector columns will be converted except nested ones.
Returns
-------
:py:class:`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
"""
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
def convertMatrixColumnsToML(dataset: DataFrame, *cols: str) -> DataFrame:
"""
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.
.. versionadded:: 2.0.0
Parameters
----------
dataset : :py:class:`pyspark.sql.DataFrame`
input dataset
\\*cols : str
Matrix columns to be converted.
New matrix columns will be ignored. If unspecified, all old
matrix columns will be converted excepted nested ones.
Returns
-------
:py:class:`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
"""
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
def convertMatrixColumnsFromML(dataset: DataFrame, *cols: str) -> DataFrame:
"""
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.
.. versionadded:: 2.0.0
Parameters
----------
dataset : :py:class:`pyspark.sql.DataFrame`
input dataset
\\*cols : str
Matrix columns to be converted.
Old matrix columns will be ignored. If unspecified, all new
matrix columns will be converted except nested ones.
Returns
-------
:py:class:`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
"""
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:
"""
Mixin for models and transformers which may be saved as files.
.. versionadded:: 1.3.0
"""
[docs] def save(self, sc: SparkContext, path: str) -> None:
"""
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`.
Parameters
----------
sc : :py:class:`pyspark.SparkContext`
Spark context used to save model data.
path : str
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
"""
_java_model: "JavaObject"
[docs] @since("1.3.0")
def save(self, sc: SparkContext, path: str) -> None:
"""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, str):
raise TypeError("path should be a string, got type %s" % type(path))
self._java_model.save(sc._jsc.sc(), path)
[docs]class Loader(Generic[T]):
"""
Mixin for classes which can load saved models from files.
.. versionadded:: 1.3.0
"""
[docs] @classmethod
def load(cls: Type[L], sc: SparkContext, path: str) -> L:
"""
Load a model from the given path. The model should have been
saved using :py:meth:`Saveable.save`.
Parameters
----------
sc : :py:class:`pyspark.SparkContext`
Spark context used for loading model files.
path : str
Path specifying the directory to which the model was saved.
Returns
-------
object
model instance
"""
raise NotImplementedError
[docs]@inherit_doc
class JavaLoader(Loader[T]):
"""
Mixin for classes which can load saved models using its Scala
implementation.
.. versionadded:: 1.3.0
"""
@classmethod
def _java_loader_class(cls) -> str:
"""
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: SparkContext, path: str) -> "JavaObject":
"""
Load a Java model from the given path.
"""
java_class = cls._java_loader_class()
java_obj: "JavaObject" = reduce(getattr, java_class.split("."), sc._jvm)
return java_obj.load(sc._jsc.sc(), path)
[docs] @classmethod
@since("1.3.0")
def load(cls: Type[JL], sc: SparkContext, path: str) -> JL:
"""Load a model from the given path."""
java_model = cls._load_java(sc, path)
return cls(java_model) # type: ignore[call-arg]
[docs]class LinearDataGenerator:
"""Utils for generating linear data.
.. versionadded:: 1.5.0
"""
[docs] @staticmethod
@since("1.5.0")
def generateLinearRDD(
sc: SparkContext,
nexamples: int,
nfeatures: int,
eps: float,
nParts: int = 2,
intercept: float = 0.0,
) -> RDD["LabeledPoint"]:
"""
Generate an RDD of LabeledPoints.
"""
return callMLlibFunc(
"generateLinearRDDWrapper",
sc,
int(nexamples),
int(nfeatures),
float(eps),
int(nParts),
float(intercept),
)
def _test() -> None:
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()