Fix variable annotation style

third_party/3/pyspark/accumulators.pyi

@@ -14,8 +14,8 @@ import socketserver as SocketServer
 from typing import Any
 
 class Accumulator(Generic[T]):
- aid = ... # type: int
- accum_param = ... # type: AccumulatorParam[T]
+ aid: int
+ accum_param: AccumulatorParam[T]
  def __init__(self, aid: int, value: T, accum_param: AccumulatorParam[T]) -> None: ...
  def __reduce__(self) -> Tuple[Callable[[int, int, AccumulatorParam[T]], Accumulator[T]], Tuple[int, int, AccumulatorParam[T]]]: ...
  @property
@@ -30,7 +30,7 @@ class AccumulatorParam(Generic[T]):
  def addInPlace(self, value1: T, value2: T) -> T: ...
 
 class AddingAccumulatorParam(AccumulatorParam[U]):
- zero_value = ... # type: U
+ zero_value: U
  def __init__(self, zero_value: U) -> None: ...
  def zero(self, value: U) -> U: ...
  def addInPlace(self, value1: U, value2: U) -> U: ...
@@ -39,7 +39,7 @@ class _UpdateRequestHandler(SocketServer.StreamRequestHandler):
  def handle(self) -> None: ...
 
 class AccumulatorServer(SocketServer.TCPServer):
- auth_token = ... # type: str
+ auth_token: str
  def __init__(self, server_address: Tuple[str, int], RequestHandlerClass: Type[socketserver.BaseRequestHandler], auth_token: str) -> None: ...
- server_shutdown: bool = ...
+ server_shutdown: bool
  def shutdown(self) -> None: ...

third_party/3/pyspark/conf.pyi

@@ -8,16 +8,16 @@ from py4j.java_gateway import JVMView, JavaObject # type: ignore
 
 class SparkConf:
  def __init__(self, loadDefaults: bool = ..., _jvm: Optional[JVMView] = ..., _jconf: Optional[JavaObject] = ...) -> None: ...
- def set(self, key: str, value: str) -> 'SparkConf': ...
- def setIfMissing(self, key: str, value: str) -> 'SparkConf': ...
- def setMaster(self, value: str) -> 'SparkConf': ...
- def setAppName(self, value: str) -> 'SparkConf': ...
- def setSparkHome(self, value: str) -> 'SparkConf': ...
+ def set(self, key: str, value: str) -> SparkConf: ...
+ def setIfMissing(self, key: str, value: str) -> SparkConf: ...
+ def setMaster(self, value: str) -> SparkConf: ...
+ def setAppName(self, value: str) -> SparkConf: ...
+ def setSparkHome(self, value: str) -> SparkConf: ...
  @overload
- def setExecutorEnv(self, key: str, value: str) -> 'SparkConf': ...
+ def setExecutorEnv(self, key: str, value: str) -> SparkConf: ...
  @overload
- def setExecutorEnv(self, *, pairs: List[Tuple[str, str]]) -> 'SparkConf': ...
- def setAll(self, pairs: List[Tuple[str, str]]) -> 'SparkConf': ...
+ def setExecutorEnv(self, *, pairs: List[Tuple[str, str]]) -> SparkConf: ...
+ def setAll(self, pairs: List[Tuple[str, str]]) -> SparkConf: ...
  def get(self, key: str, defaultValue: Optional[str] = ...) -> str: ...
  def getAll(self) -> List[Tuple[str, str]]: ...
  def contains(self, key: str) -> bool: ...

third_party/3/pyspark/context.pyi

@@ -18,13 +18,13 @@ T = TypeVar('T')
 U = TypeVar('U')
 
 class SparkContext:
- PACKAGE_EXTENSIONS = ... # type: Iterable[str]
+ PACKAGE_EXTENSIONS: Iterable[str]
  def __init__(self, master: Optional[str] = ..., appName: Optional[str] = ..., sparkHome: Optional[str] = ..., pyFiles: Optional[List[str]] = ..., environment: Optional[Dict[str, str]] = ..., batchSize: int = ..., serializer: Serializer = ..., conf: Optional[SparkConf] = ..., gateway: Optional[JavaGateway] = ..., jsc: Optional[JavaObject] = ..., profiler_cls: type = ...) -> None: ...
  def __getnewargs__(self): ...
  def __enter__(self): ...
  def __exit__(self, type, value, trace): ...
  @classmethod
- def getOrCreate(cls, conf: Optional[SparkConf] = ...) -> 'SparkContext': ...
+ def getOrCreate(cls, conf: Optional[SparkConf] = ...) -> SparkContext: ...
  def setLogLevel(self, logLevel: str) -> None: ...
  @classmethod
  def setSystemProperty(cls, key: str, value: str) -> None: ...

third_party/3/pyspark/ml/base.pyi

@@ -17,29 +17,29 @@ M = TypeVar("M", bound=Transformer)
 ParamMap = Dict[Param, Any]
 
 class _FitMultipleIterator:
- fitSingleModel = ... # type: Callable[[int], Transformer]
- numModel = ... # type: int
+ fitSingleModel: Callable[[int], Transformer]
+ numModel: int
  counter: int = ...
- lock = ... # type: _thread.LockType
+ lock: _thread.LockType
  def __init__(self, fitSingleModel: Callable[[int], Transformer], numModels: int) -> None: ...
  def __iter__(self) -> _FitMultipleIterator: ...
  def __next__(self) -> Tuple[int, Transformer]: ...
  def next(self) -> Tuple[int, Transformer]: ...
 
 class Estimator(Params, Generic[M]):
- __metaclass__ = ... # type: Type[abc.ABCMeta]
+ __metaclass__: Type[abc.ABCMeta]
  @overload
  def fit(self, dataset: DataFrame, params: Optional[ParamMap] = ...) -> M: ...
  @overload
  def fit(self, dataset: DataFrame, params: List[ParamMap]) -> List[M]: ...
  def fitMultiple(self, dataset: DataFrame, params: List[ParamMap]) -> Iterable[Tuple[int, M]]: ...
 
 class Transformer(Params):
- __metaclass__ = ... # type: Type[abc.ABCMeta]
+ __metaclass__: Type[abc.ABCMeta]
  def transform(self, dataset: DataFrame, params: Optional[ParamMap] = ...) -> DataFrame: ...
 
 class Model(Transformer):
- __metaclass__ = ... # type: Type[abc.ABCMeta]
+ __metaclass__: Type[abc.ABCMeta]
 
 class UnaryTransformer(HasInputCol, HasOutputCol, Transformer):
  def createTransformFunc(self) -> Callable: ...

third_party/3/pyspark/ml/classification.pyi

@@ -22,7 +22,7 @@ class JavaClassificationModel(JavaPredictionModel):
 
 class LinearSVC(JavaEstimator[LinearSVCModel], HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasRegParam, HasTol, HasRawPredictionCol, HasFitIntercept, HasStandardization, HasThreshold, HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable):
  def __init__(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., tol: float = ..., rawPredictionCol: str = ..., fitIntercept: bool = ..., standardization: bool = ..., threshold: float = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ...) -> None: ...
- def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., tol: float = ..., rawPredictionCol: str = ..., fitIntercept: bool = ..., standardization: bool = ..., threshold: float = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ...) -> 'LinearSVC': ...
+ def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., tol: float = ..., rawPredictionCol: str = ..., fitIntercept: bool = ..., standardization: bool = ..., threshold: float = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ...) -> LinearSVC: ...
 
 class LinearSVCModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
  @property
@@ -31,27 +31,27 @@ class LinearSVCModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLR
  def intercept(self) -> float: ...
 
 class LogisticRegression(JavaEstimator[LogisticRegressionModel], HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol, HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds, HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable):
- threshold = ... # type: Param
- family = ... # type: Param
- lowerBoundsOnCoefficients = ... # type: Param
- upperBoundsOnCoefficients = ... # type: Param
- lowerBoundsOnIntercepts = ... # type: Param
- upperBoundsOnIntercepts = ... # type: Param
+ threshold: Param
+ family: Param
+ lowerBoundsOnCoefficients: Param
+ upperBoundsOnCoefficients: Param
+ lowerBoundsOnIntercepts: Param
+ upperBoundsOnIntercepts: Param
  def __init__(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> None: ...
- def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> 'LogisticRegression': ...
- def setThreshold(self, value: float) -> 'LogisticRegression': ...
+ def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., regParam: float = ..., elasticNetParam: float = ..., tol: float = ..., fitIntercept: bool = ..., threshold: float = ..., thresholds: Optional[List[float]] = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., standardization: bool = ..., weightCol: Optional[str] = ..., aggregationDepth: int = ..., family: str = ..., lowerBoundsOnCoefficients: Optional[Matrix] = ..., upperBoundsOnCoefficients: Optional[Matrix] = ..., lowerBoundsOnIntercepts: Optional[Vector] = ..., upperBoundsOnIntercepts: Optional[Vector] = ...) -> LogisticRegression: ...
+ def setThreshold(self, value: float) -> LogisticRegression: ...
  def getThreshold(self) -> float: ...
- def setThresholds(self, value: List[float]) -> 'LogisticRegression': ...
+ def setThresholds(self, value: List[float]) -> LogisticRegression: ...
  def getThresholds(self) -> List[float]: ...
- def setFamily(self, value: str) -> 'LogisticRegression': ...
+ def setFamily(self, value: str) -> LogisticRegression: ...
  def getFamily(self) -> str: ...
- def setLowerBoundsOnCoefficients(self, value: Matrix) -> 'LogisticRegression': ...
+ def setLowerBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ...
  def getLowerBoundsOnCoefficients(self) -> Matrix : ...
- def setUpperBoundsOnCoefficients(self, value: Matrix) -> 'LogisticRegression': ...
+ def setUpperBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ...
  def getUpperBoundsOnCoefficients(self) -> Matrix: ...
- def setLowerBoundsOnIntercepts(self, value: Vector) -> 'LogisticRegression': ...
+ def setLowerBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ...
  def getLowerBoundsOnIntercepts(self) -> Vector: ...
- def setUpperBoundsOnIntercepts(self, value: Vector) -> 'LogisticRegression': ...
+ def setUpperBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ...
  def getUpperBoundsOnIntercepts(self) -> Vector: ...
 
 class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable, HasTrainingSummary):
@@ -126,8 +126,8 @@ class BinaryLogisticRegressionSummary(LogisticRegressionSummary):
 class BinaryLogisticRegressionTrainingSummary(BinaryLogisticRegressionSummary, LogisticRegressionTrainingSummary): ...
 
 class TreeClassifierParams:
- supportedImpurities = ... # type: List[str]
- impurity = ... # type: Param
+ supportedImpurities: List[str]
+ impurity: Param
  def __init__(self) -> None: ...
  def getImpurity(self) -> str: ...
 
@@ -167,8 +167,8 @@ class RandomForestClassificationModel(TreeEnsembleModel, JavaClassificationModel
  def trees(self) -> List[DecisionTreeClassificationModel]: ...
 
 class GBTClassifierParams(GBTParams, HasVarianceImpurity):
- supportedLossTypes = ... # type: List[str]
- lossType = ... # type: Param
+ supportedLossTypes: List[str]
+ lossType: Param
  def getLossType(self) -> str: ...
 
 class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, GBTClassifierParams, HasCheckpointInterval, HasSeed, JavaMLWritable, JavaMLReadable):
@@ -194,13 +194,13 @@ class GBTClassificationModel(TreeEnsembleModel, JavaClassificationModel, JavaMLW
  def evaluateEachIteration(self, dataset: DataFrame) -> List[float]: ...
 
 class NaiveBayes(JavaEstimator[NaiveBayesModel], HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasThresholds, HasWeightCol, JavaMLWritable, JavaMLReadable):
- smoothing = ... # type: Param
- modelType = ... # type: Param
+ smoothing: Param
+ modelType: Param
  def __init__(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., smoothing: float = ..., modelType: str = ..., thresholds: Optional[List[float]] = ..., weightCol: Optional[str] = ...) -> None: ...
- def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., smoothing: float = ..., modelType: str = ..., thresholds: Optional[List[float]] = ..., weightCol: Optional[str] = ...) -> 'NaiveBayes': ...
- def setSmoothing(self, value: float) -> 'NaiveBayes': ...
+ def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., probabilityCol: str = ..., rawPredictionCol: str = ..., smoothing: float = ..., modelType: str = ..., thresholds: Optional[List[float]] = ..., weightCol: Optional[str] = ...) -> NaiveBayes: ...
+ def setSmoothing(self, value: float) -> NaiveBayes: ...
  def getSmoothing(self) -> float: ...
- def setModelType(self, value: str) -> 'NaiveBayes': ...
+ def setModelType(self, value: str) -> NaiveBayes: ...
  def getModelType(self) -> str: ...
 
 class NaiveBayesModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
@@ -210,19 +210,19 @@ class NaiveBayesModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaML
  def theta(self) -> Matrix: ...
 
 class MultilayerPerceptronClassifier(JavaEstimator[MultilayerPerceptronClassificationModel], HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed, HasStepSize, HasSolver, JavaMLWritable, JavaMLReadable, HasProbabilityCol, HasRawPredictionCol):
- layers = ... # type: Param
- blockSize = ... # type: Param
- solver = ... # type: Param
- initialWeights = ... # type: Param
+ layers: Param
+ blockSize: Param
+ solver: Param
+ initialWeights: Param
  def __init__(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., tol: float = ..., seed: Optional[int] = ..., layers: Optional[List[int]] = ..., blockSize: int = ..., stepSize: float = ..., solver: str = ..., initialWeights: Optional[Vector] = ..., probabilityCol: str = ..., rawPredictionCol: str = ...) -> None: ...
- def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., tol: float = ..., seed: Optional[int] = ..., layers: Optional[List[int]] = ..., blockSize: int = ..., stepSize: float = ..., solver: str = ..., initialWeights: Optional[Vector] = ..., probabilityCol: str = ..., rawPredictionCol: str = ...) -> 'MultilayerPerceptronClassifier': ...
- def setLayers(self, value: List[int]) -> 'MultilayerPerceptronClassifier': ...
+ def setParams(self, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., maxIter: int = ..., tol: float = ..., seed: Optional[int] = ..., layers: Optional[List[int]] = ..., blockSize: int = ..., stepSize: float = ..., solver: str = ..., initialWeights: Optional[Vector] = ..., probabilityCol: str = ..., rawPredictionCol: str = ...) -> MultilayerPerceptronClassifier: ...
+ def setLayers(self, value: List[int]) -> MultilayerPerceptronClassifier: ...
  def getLayers(self) -> List[int]: ...
- def setBlockSize(self, value: int) -> 'MultilayerPerceptronClassifier': ...
+ def setBlockSize(self, value: int) -> MultilayerPerceptronClassifier: ...
  def getBlockSize(self) -> int: ...
- def setStepSize(self, value: float) -> 'MultilayerPerceptronClassifier': ...
+ def setStepSize(self, value: float) -> MultilayerPerceptronClassifier: ...
  def getStepSize(self) -> float: ...
- def setInitialWeights(self, value: Vector) -> 'MultilayerPerceptronClassifier': ...
+ def setInitialWeights(self, value: Vector) -> MultilayerPerceptronClassifier: ...
  def getInitialWeights(self) -> Vector: ...
 
 class MultilayerPerceptronClassificationModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
@@ -232,7 +232,7 @@ class MultilayerPerceptronClassificationModel(JavaModel, JavaClassificationModel
  def weights(self) -> Vector: ...
 
 class OneVsRestParams(HasFeaturesCol, HasLabelCol, HasWeightCol, HasPredictionCol, HasRawPredictionCol):
- classifier = ... # type: Param
+ classifier: Param
  def setClassifier(self, value: Estimator[M]) -> OneVsRestParams: ...
  def getClassifier(self) -> Estimator[M]: ...
 
@@ -242,6 +242,6 @@ class OneVsRest(Estimator[OneVsRestModel], OneVsRestParams, HasParallelism, Java
  def copy(self, extra: Optional[ParamMap] = ...) -> OneVsRest: ...
 
 class OneVsRestModel(Model, OneVsRestParams, JavaMLReadable, JavaMLWritable):
- models = ... # type: List[Transformer]
+ models: List[Transformer]
  def __init__(self, models: List[Transformer]) -> None: ...
- def copy(self, extra: Optional[ParamMap] = ...) -> 'OneVsRestModel': ...
+ def copy(self, extra: Optional[ParamMap] = ...) -> OneVsRestModel: ...