#
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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#
"""
Python package for feature in MLlib.
"""
import sys
import warnings
from typing import Dict, Hashable, Iterable, List, Optional, Tuple, Union, overload, TYPE_CHECKING
from py4j.protocol import Py4JJavaError
from pyspark import since
from pyspark.core.rdd import RDD
from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
from pyspark.mllib.linalg import Vectors, _convert_to_vector
from pyspark.mllib.util import JavaLoader, JavaSaveable
from pyspark.core.context import SparkContext
from pyspark.mllib.linalg import Vector
from pyspark.mllib.regression import LabeledPoint
from py4j.java_collections import JavaMap
if TYPE_CHECKING:
from pyspark.mllib._typing import VectorLike
from py4j.java_collections import JavaMap
__all__ = [
"Normalizer",
"StandardScalerModel",
"StandardScaler",
"HashingTF",
"IDFModel",
"IDF",
"Word2Vec",
"Word2VecModel",
"ChiSqSelector",
"ChiSqSelectorModel",
"ElementwiseProduct",
]
class VectorTransformer:
"""
Base class for transformation of a vector or RDD of vector
"""
@overload
def transform(self, vector: "VectorLike") -> Vector:
...
@overload
def transform(self, vector: RDD["VectorLike"]) -> RDD[Vector]:
...
def transform(
self, vector: Union["VectorLike", RDD["VectorLike"]]
) -> Union[Vector, RDD[Vector]]:
"""
Applies transformation on a vector.
Parameters
----------
vector : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD`
vector or convertible or RDD to be transformed.
"""
raise NotImplementedError
[docs]class Normalizer(VectorTransformer):
r"""
Normalizes samples individually to unit L\ :sup:`p`\ norm
For any 1 <= `p` < float('inf'), normalizes samples using
sum(abs(vector) :sup:`p`) :sup:`(1/p)` as norm.
For `p` = float('inf'), max(abs(vector)) will be used as norm for
normalization.
.. versionadded:: 1.2.0
Parameters
----------
p : float, optional
Normalization in L^p^ space, p = 2 by default.
Examples
--------
>>> from pyspark.mllib.linalg import Vectors
>>> v = Vectors.dense(range(3))
>>> nor = Normalizer(1)
>>> nor.transform(v)
DenseVector([0.0, 0.3333, 0.6667])
>>> rdd = sc.parallelize([v])
>>> nor.transform(rdd).collect()
[DenseVector([0.0, 0.3333, 0.6667])]
>>> nor2 = Normalizer(float("inf"))
>>> nor2.transform(v)
DenseVector([0.0, 0.5, 1.0])
"""
def __init__(self, p: float = 2.0):
assert p >= 1.0, "p should be greater than 1.0"
self.p = float(p)
@overload
def transform(self, vector: "VectorLike") -> Vector:
...
@overload
def transform(self, vector: RDD["VectorLike"]) -> RDD[Vector]:
...
class JavaVectorTransformer(JavaModelWrapper, VectorTransformer):
"""
Wrapper for the model in JVM
"""
@overload
def transform(self, vector: "VectorLike") -> Vector:
...
@overload
def transform(self, vector: RDD["VectorLike"]) -> RDD[Vector]:
...
def transform(
self, vector: Union["VectorLike", RDD["VectorLike"]]
) -> Union[Vector, RDD[Vector]]:
"""
Applies transformation on a vector or an RDD[Vector].
Parameters
----------
vector : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD`
Input vector(s) to be transformed.
Notes
-----
In Python, transform cannot currently be used within
an RDD transformation or action.
Call transform directly on the RDD instead.
"""
if isinstance(vector, RDD):
vector = vector.map(_convert_to_vector)
else:
vector = _convert_to_vector(vector)
return self.call("transform", vector)
[docs]class StandardScalerModel(JavaVectorTransformer):
"""
Represents a StandardScaler model that can transform vectors.
.. versionadded:: 1.2.0
"""
@overload
def transform(self, vector: "VectorLike") -> Vector:
...
@overload
def transform(self, vector: RDD["VectorLike"]) -> RDD[Vector]:
...
[docs] @since("1.4.0")
def setWithMean(self, withMean: bool) -> "StandardScalerModel":
"""
Setter of the boolean which decides
whether it uses mean or not
"""
self.call("setWithMean", withMean)
return self
[docs] @since("1.4.0")
def setWithStd(self, withStd: bool) -> "StandardScalerModel":
"""
Setter of the boolean which decides
whether it uses std or not
"""
self.call("setWithStd", withStd)
return self
@property
@since("2.0.0")
def withStd(self) -> bool:
"""
Returns if the model scales the data to unit standard deviation.
"""
return self.call("withStd")
@property
@since("2.0.0")
def withMean(self) -> bool:
"""
Returns if the model centers the data before scaling.
"""
return self.call("withMean")
@property
@since("2.0.0")
def std(self) -> Vector:
"""
Return the column standard deviation values.
"""
return self.call("std")
@property
@since("2.0.0")
def mean(self) -> Vector:
"""
Return the column mean values.
"""
return self.call("mean")
[docs]class StandardScaler:
"""
Standardizes features by removing the mean and scaling to unit
variance using column summary statistics on the samples in the
training set.
.. versionadded:: 1.2.0
Parameters
----------
withMean : bool, optional
False by default. Centers the data with mean
before scaling. It will build a dense output, so take
care when applying to sparse input.
withStd : bool, optional
True by default. Scales the data to unit
standard deviation.
Examples
--------
>>> vs = [Vectors.dense([-2.0, 2.3, 0]), Vectors.dense([3.8, 0.0, 1.9])]
>>> dataset = sc.parallelize(vs)
>>> standardizer = StandardScaler(True, True)
>>> model = standardizer.fit(dataset)
>>> result = model.transform(dataset)
>>> for r in result.collect(): r
DenseVector([-0.7071, 0.7071, -0.7071])
DenseVector([0.7071, -0.7071, 0.7071])
>>> int(model.std[0])
4
>>> int(model.mean[0]*10)
9
>>> model.withStd
True
>>> model.withMean
True
"""
def __init__(self, withMean: bool = False, withStd: bool = True):
if not (withMean or withStd):
warnings.warn("Both withMean and withStd are false. The model does nothing.")
self.withMean = withMean
self.withStd = withStd
[docs] def fit(self, dataset: RDD["VectorLike"]) -> "StandardScalerModel":
"""
Computes the mean and variance and stores as a model to be used
for later scaling.
.. versionadded:: 1.2.0
Parameters
----------
dataset : :py:class:`pyspark.RDD`
The data used to compute the mean and variance
to build the transformation model.
Returns
-------
:py:class:`StandardScalerModel`
"""
dataset = dataset.map(_convert_to_vector)
jmodel = callMLlibFunc("fitStandardScaler", self.withMean, self.withStd, dataset)
return StandardScalerModel(jmodel)
[docs]class ChiSqSelectorModel(JavaVectorTransformer):
"""
Represents a Chi Squared selector model.
.. versionadded:: 1.4.0
"""
@overload
def transform(self, vector: "VectorLike") -> Vector:
...
@overload
def transform(self, vector: RDD["VectorLike"]) -> RDD[Vector]:
...
[docs]class ChiSqSelector:
"""
Creates a ChiSquared feature selector.
The selector supports different selection methods: `numTopFeatures`, `percentile`, `fpr`,
`fdr`, `fwe`.
* `numTopFeatures` chooses a fixed number of top features according to a chi-squared test.
* `percentile` is similar but chooses a fraction of all features
instead of a fixed number.
* `fpr` chooses all features whose p-values are below a threshold,
thus controlling the false positive rate of selection.
* `fdr` uses the `Benjamini-Hochberg procedure <https://en.wikipedia.org/wiki/
False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure>`_
to choose all features whose false discovery rate is below a threshold.
* `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by
1/numFeatures, thus controlling the family-wise error rate of selection.
By default, the selection method is `numTopFeatures`, with the default number of top features
set to 50.
.. versionadded:: 1.4.0
Examples
--------
>>> from pyspark.mllib.linalg import SparseVector, DenseVector
>>> from pyspark.mllib.regression import LabeledPoint
>>> data = sc.parallelize([
... LabeledPoint(0.0, SparseVector(3, {0: 8.0, 1: 7.0})),
... LabeledPoint(1.0, SparseVector(3, {1: 9.0, 2: 6.0})),
... LabeledPoint(1.0, [0.0, 9.0, 8.0]),
... LabeledPoint(2.0, [7.0, 9.0, 5.0]),
... LabeledPoint(2.0, [8.0, 7.0, 3.0])
... ])
>>> model = ChiSqSelector(numTopFeatures=1).fit(data)
>>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0}))
SparseVector(1, {})
>>> model.transform(DenseVector([7.0, 9.0, 5.0]))
DenseVector([7.0])
>>> model = ChiSqSelector(selectorType="fpr", fpr=0.2).fit(data)
>>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0}))
SparseVector(1, {})
>>> model.transform(DenseVector([7.0, 9.0, 5.0]))
DenseVector([7.0])
>>> model = ChiSqSelector(selectorType="percentile", percentile=0.34).fit(data)
>>> model.transform(DenseVector([7.0, 9.0, 5.0]))
DenseVector([7.0])
"""
def __init__(
self,
numTopFeatures: int = 50,
selectorType: str = "numTopFeatures",
percentile: float = 0.1,
fpr: float = 0.05,
fdr: float = 0.05,
fwe: float = 0.05,
):
self.numTopFeatures = numTopFeatures
self.selectorType = selectorType
self.percentile = percentile
self.fpr = fpr
self.fdr = fdr
self.fwe = fwe
[docs] @since("2.1.0")
def setNumTopFeatures(self, numTopFeatures: int) -> "ChiSqSelector":
"""
set numTopFeature for feature selection by number of top features.
Only applicable when selectorType = "numTopFeatures".
"""
self.numTopFeatures = int(numTopFeatures)
return self
[docs] @since("2.1.0")
def setPercentile(self, percentile: float) -> "ChiSqSelector":
"""
set percentile [0.0, 1.0] for feature selection by percentile.
Only applicable when selectorType = "percentile".
"""
self.percentile = float(percentile)
return self
[docs] @since("2.1.0")
def setFpr(self, fpr: float) -> "ChiSqSelector":
"""
set FPR [0.0, 1.0] for feature selection by FPR.
Only applicable when selectorType = "fpr".
"""
self.fpr = float(fpr)
return self
[docs] @since("2.2.0")
def setFdr(self, fdr: float) -> "ChiSqSelector":
"""
set FDR [0.0, 1.0] for feature selection by FDR.
Only applicable when selectorType = "fdr".
"""
self.fdr = float(fdr)
return self
[docs] @since("2.2.0")
def setFwe(self, fwe: float) -> "ChiSqSelector":
"""
set FWE [0.0, 1.0] for feature selection by FWE.
Only applicable when selectorType = "fwe".
"""
self.fwe = float(fwe)
return self
[docs] @since("2.1.0")
def setSelectorType(self, selectorType: str) -> "ChiSqSelector":
"""
set the selector type of the ChisqSelector.
Supported options: "numTopFeatures" (default), "percentile", "fpr", "fdr", "fwe".
"""
self.selectorType = str(selectorType)
return self
[docs] def fit(self, data: RDD[LabeledPoint]) -> "ChiSqSelectorModel":
"""
Returns a ChiSquared feature selector.
.. versionadded:: 1.4.0
Parameters
----------
data : :py:class:`pyspark.RDD` of :py:class:`pyspark.mllib.regression.LabeledPoint`
containing the labeled dataset with categorical features.
Real-valued features will be treated as categorical for each
distinct value. Apply feature discretizer before using this function.
"""
jmodel = callMLlibFunc(
"fitChiSqSelector",
self.selectorType,
self.numTopFeatures,
self.percentile,
self.fpr,
self.fdr,
self.fwe,
data,
)
return ChiSqSelectorModel(jmodel)
class PCAModel(JavaVectorTransformer):
"""
Model fitted by [[PCA]] that can project vectors to a low-dimensional space using PCA.
.. versionadded:: 1.5.0
"""
class PCA:
"""
A feature transformer that projects vectors to a low-dimensional space using PCA.
.. versionadded:: 1.5.0
Examples
--------
>>> data = [Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),
... Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),
... Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0])]
>>> model = PCA(2).fit(sc.parallelize(data))
>>> pcArray = model.transform(Vectors.sparse(5, [(1, 1.0), (3, 7.0)])).toArray()
>>> float(pcArray[0])
1.648...
>>> float(pcArray[1])
-4.013...
"""
def __init__(self, k: int):
"""
Parameters
----------
k : int
number of principal components.
"""
self.k = int(k)
def fit(self, data: RDD["VectorLike"]) -> PCAModel:
"""
Computes a [[PCAModel]] that contains the principal components of the input vectors.
.. versionadded:: 1.5.0
Parameters
----------
data : :py:class:`pyspark.RDD`
source vectors
"""
jmodel = callMLlibFunc("fitPCA", self.k, data)
return PCAModel(jmodel)
[docs]class HashingTF:
"""
Maps a sequence of terms to their term frequencies using the hashing
trick.
.. versionadded:: 1.2.0
Parameters
----------
numFeatures : int, optional
number of features (default: 2^20)
Notes
-----
The terms must be hashable (can not be dict/set/list...).
Examples
--------
>>> htf = HashingTF(100)
>>> doc = "a a b b c d".split(" ")
>>> htf.transform(doc)
SparseVector(100, {...})
"""
def __init__(self, numFeatures: int = 1 << 20):
self.numFeatures = numFeatures
self.binary = False
[docs] @since("2.0.0")
def setBinary(self, value: bool) -> "HashingTF":
"""
If True, term frequency vector will be binary such that non-zero
term counts will be set to 1
(default: False)
"""
self.binary = value
return self
[docs] @since("1.2.0")
def indexOf(self, term: Hashable) -> int:
"""Returns the index of the input term."""
return hash(term) % self.numFeatures
@overload
def transform(self, document: Iterable[Hashable]) -> Vector:
...
@overload
def transform(self, document: RDD[Iterable[Hashable]]) -> RDD[Vector]:
...
[docs]class IDFModel(JavaVectorTransformer):
"""
Represents an IDF model that can transform term frequency vectors.
.. versionadded:: 1.2.0
"""
@overload
def transform(self, x: "VectorLike") -> Vector:
...
@overload
def transform(self, x: RDD["VectorLike"]) -> RDD[Vector]:
...
[docs] @since("1.4.0")
def idf(self) -> Vector:
"""
Returns the current IDF vector.
"""
return self.call("idf")
[docs] @since("3.0.0")
def docFreq(self) -> List[int]:
"""
Returns the document frequency.
"""
return self.call("docFreq")
[docs] @since("3.0.0")
def numDocs(self) -> int:
"""
Returns number of documents evaluated to compute idf
"""
return self.call("numDocs")
[docs]class IDF:
"""
Inverse document frequency (IDF).
The standard formulation is used: `idf = log((m + 1) / (d(t) + 1))`,
where `m` is the total number of documents and `d(t)` is the number
of documents that contain term `t`.
This implementation supports filtering out terms which do not appear
in a minimum number of documents (controlled by the variable
`minDocFreq`). For terms that are not in at least `minDocFreq`
documents, the IDF is found as 0, resulting in TF-IDFs of 0.
.. versionadded:: 1.2.0
Parameters
----------
minDocFreq : int
minimum of documents in which a term should appear for filtering
Examples
--------
>>> n = 4
>>> freqs = [Vectors.sparse(n, (1, 3), (1.0, 2.0)),
... Vectors.dense([0.0, 1.0, 2.0, 3.0]),
... Vectors.sparse(n, [1], [1.0])]
>>> data = sc.parallelize(freqs)
>>> idf = IDF()
>>> model = idf.fit(data)
>>> tfidf = model.transform(data)
>>> for r in tfidf.collect(): r
SparseVector(4, {1: 0.0, 3: 0.5754})
DenseVector([0.0, 0.0, 1.3863, 0.863])
SparseVector(4, {1: 0.0})
>>> model.transform(Vectors.dense([0.0, 1.0, 2.0, 3.0]))
DenseVector([0.0, 0.0, 1.3863, 0.863])
>>> model.transform([0.0, 1.0, 2.0, 3.0])
DenseVector([0.0, 0.0, 1.3863, 0.863])
>>> model.transform(Vectors.sparse(n, (1, 3), (1.0, 2.0)))
SparseVector(4, {1: 0.0, 3: 0.5754})
"""
def __init__(self, minDocFreq: int = 0):
self.minDocFreq = minDocFreq
[docs] def fit(self, dataset: RDD["VectorLike"]) -> IDFModel:
"""
Computes the inverse document frequency.
.. versionadded:: 1.2.0
Parameters
----------
dataset : :py:class:`pyspark.RDD`
an RDD of term frequency vectors
"""
if not isinstance(dataset, RDD):
raise TypeError("dataset should be an RDD of term frequency vectors")
jmodel = callMLlibFunc("fitIDF", self.minDocFreq, dataset.map(_convert_to_vector))
return IDFModel(jmodel)
[docs]class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader["Word2VecModel"]):
"""
class for Word2Vec model
"""
[docs] def findSynonyms(self, word: Union[str, "VectorLike"], num: int) -> Iterable[Tuple[str, float]]:
"""
Find synonyms of a word
.. versionadded:: 1.2.0
Parameters
----------
word : str or :py:class:`pyspark.mllib.linalg.Vector`
a word or a vector representation of word
num : int
number of synonyms to find
Returns
-------
:py:class:`collections.abc.Iterable`
array of (word, cosineSimilarity)
Notes
-----
Local use only
"""
if not isinstance(word, str):
word = _convert_to_vector(word)
words, similarity = self.call("findSynonyms", word, num)
return zip(words, similarity)
[docs] @since("1.4.0")
def getVectors(self) -> "JavaMap":
"""
Returns a map of words to their vector representations.
"""
return self.call("getVectors")
[docs] @classmethod
@since("1.5.0")
def load(cls, sc: SparkContext, path: str) -> "Word2VecModel":
"""
Load a model from the given path.
"""
assert sc._jvm is not None
jmodel = sc._jvm.org.apache.spark.mllib.feature.Word2VecModel.load(sc._jsc.sc(), path)
model = sc._jvm.org.apache.spark.mllib.api.python.Word2VecModelWrapper(jmodel)
return Word2VecModel(model)
[docs]class Word2Vec:
"""Word2Vec creates vector representation of words in a text corpus.
The algorithm first constructs a vocabulary from the corpus
and then learns vector representation of words in the vocabulary.
The vector representation can be used as features in
natural language processing and machine learning algorithms.
We used skip-gram model in our implementation and hierarchical
softmax method to train the model. The variable names in the
implementation matches the original C implementation.
For original C implementation,
see https://code.google.com/p/word2vec/
For research papers, see
Efficient Estimation of Word Representations in Vector Space
and Distributed Representations of Words and Phrases and their
Compositionality.
.. versionadded:: 1.2.0
Examples
--------
>>> sentence = "a b " * 100 + "a c " * 10
>>> localDoc = [sentence, sentence]
>>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" "))
>>> model = Word2Vec().setVectorSize(10).setSeed(42).fit(doc)
Querying for synonyms of a word will not return that word:
>>> syms = model.findSynonyms("a", 2)
>>> [s[0] for s in syms]
['b', 'c']
But querying for synonyms of a vector may return the word whose
representation is that vector:
>>> vec = model.transform("a")
>>> syms = model.findSynonyms(vec, 2)
>>> [s[0] for s in syms]
['a', 'b']
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = Word2VecModel.load(sc, path)
>>> model.transform("a") == sameModel.transform("a")
True
>>> syms = sameModel.findSynonyms("a", 2)
>>> [s[0] for s in syms]
['b', 'c']
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except OSError:
... pass
"""
def __init__(self) -> None:
"""
Construct Word2Vec instance
"""
self.vectorSize = 100
self.learningRate = 0.025
self.numPartitions = 1
self.numIterations = 1
self.seed: Optional[int] = None
self.minCount = 5
self.windowSize = 5
[docs] @since("1.2.0")
def setVectorSize(self, vectorSize: int) -> "Word2Vec":
"""
Sets vector size (default: 100).
"""
self.vectorSize = vectorSize
return self
[docs] @since("1.2.0")
def setLearningRate(self, learningRate: float) -> "Word2Vec":
"""
Sets initial learning rate (default: 0.025).
"""
self.learningRate = learningRate
return self
[docs] @since("1.2.0")
def setNumPartitions(self, numPartitions: int) -> "Word2Vec":
"""
Sets number of partitions (default: 1). Use a small number for
accuracy.
"""
self.numPartitions = numPartitions
return self
[docs] @since("1.2.0")
def setNumIterations(self, numIterations: int) -> "Word2Vec":
"""
Sets number of iterations (default: 1), which should be smaller
than or equal to number of partitions.
"""
self.numIterations = numIterations
return self
[docs] @since("1.2.0")
def setSeed(self, seed: int) -> "Word2Vec":
"""
Sets random seed.
"""
self.seed = seed
return self
[docs] @since("1.4.0")
def setMinCount(self, minCount: int) -> "Word2Vec":
"""
Sets minCount, the minimum number of times a token must appear
to be included in the word2vec model's vocabulary (default: 5).
"""
self.minCount = minCount
return self
[docs] @since("2.0.0")
def setWindowSize(self, windowSize: int) -> "Word2Vec":
"""
Sets window size (default: 5).
"""
self.windowSize = windowSize
return self
[docs] def fit(self, data: RDD[List[str]]) -> "Word2VecModel":
"""
Computes the vector representation of each word in vocabulary.
.. versionadded:: 1.2.0
Parameters
----------
data : :py:class:`pyspark.RDD`
training data. RDD of list of string
Returns
-------
:py:class:`Word2VecModel`
"""
if not isinstance(data, RDD):
raise TypeError("data should be an RDD of list of string")
jmodel = callMLlibFunc(
"trainWord2VecModel",
data,
int(self.vectorSize),
float(self.learningRate),
int(self.numPartitions),
int(self.numIterations),
self.seed,
int(self.minCount),
int(self.windowSize),
)
return Word2VecModel(jmodel)
[docs]class ElementwiseProduct(VectorTransformer):
"""
Scales each column of the vector, with the supplied weight vector.
i.e the elementwise product.
.. versionadded:: 1.5.0
Examples
--------
>>> weight = Vectors.dense([1.0, 2.0, 3.0])
>>> eprod = ElementwiseProduct(weight)
>>> a = Vectors.dense([2.0, 1.0, 3.0])
>>> eprod.transform(a)
DenseVector([2.0, 2.0, 9.0])
>>> b = Vectors.dense([9.0, 3.0, 4.0])
>>> rdd = sc.parallelize([a, b])
>>> eprod.transform(rdd).collect()
[DenseVector([2.0, 2.0, 9.0]), DenseVector([9.0, 6.0, 12.0])]
"""
def __init__(self, scalingVector: Vector) -> None:
self.scalingVector = _convert_to_vector(scalingVector)
@overload
def transform(self, vector: "VectorLike") -> Vector:
...
@overload
def transform(self, vector: RDD["VectorLike"]) -> RDD[Vector]:
...
def _test() -> None:
import doctest
from pyspark.sql import SparkSession
globs = globals().copy()
spark = SparkSession.builder.master("local[4]").appName("mllib.feature tests").getOrCreate()
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__":
sys.path.pop(0)
_test()