diff --git a/third_party/3/pyspark/ml/base.pyi b/third_party/3/pyspark/ml/base.pyi index 16edf396..099f2e9b 100644 --- a/third_party/3/pyspark/ml/base.pyi +++ b/third_party/3/pyspark/ml/base.pyi @@ -1,8 +1,7 @@ -# Stubs for pyspark.ml.base (Python 3.5) - import abc from typing import overload from typing import Any, Callable, Dict, Generic, Iterable, List, Optional, Tuple, Type, TypeVar +from pyspark.ml._typing import M import _thread @@ -43,3 +42,5 @@ class UnaryTransformer(HasInputCol, HasOutputCol, Transformer): def outputDataType(self) -> DataType: ... def validateInputType(self, inputType: DataType) -> None: ... def transformSchema(self, schema: StructType) -> StructType: ... + def setInputCol(self: M, value: str) -> M: ... + def setOutputCol(self: M, value: str) -> M: ... diff --git a/third_party/3/pyspark/ml/classification.pyi b/third_party/3/pyspark/ml/classification.pyi index 8bfef4f4..68d7063d 100644 --- a/third_party/3/pyspark/ml/classification.pyi +++ b/third_party/3/pyspark/ml/classification.pyi @@ -39,8 +39,17 @@ class _LinearSVCParams(_JavaClassifierParams, HasRegParam, HasMaxIter, HasFitInt class LinearSVC(JavaClassifier[LinearSVCModel], _LinearSVCParams, JavaMLWritable, JavaMLReadable[LinearSVC]): 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 setMaxIter(self, value: int) -> LinearSVC: ... + def setRegParam(self, value: float) -> LinearSVC: ... + def setTol(self, value: float) -> LinearSVC: ... + def setFitIntercept(self, value: bool) -> LinearSVC: ... + def setStandardization(self, value: bool) -> LinearSVC: ... + def setThreshold(self, value: float) -> LinearSVC: ... + def setWeightCol(self, value: str) -> LinearSVC: ... + def setAggregationDepth(self, value: int) -> LinearSVC: ... class LinearSVCModel(JavaClassificationModel[Vector], _LinearSVCParams, JavaMLWritable, JavaMLReadable[LinearSVCModel]): + def setThreshold(self, value: float) -> LinearSVCModel: ... @property def coefficients(self) -> Vector: ... @property @@ -71,6 +80,14 @@ class LogisticRegression(JavaProbabilisticClassifier[LogisticRegressionModel], _ def setUpperBoundsOnCoefficients(self, value: Matrix) -> LogisticRegression: ... def setLowerBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ... def setUpperBoundsOnIntercepts(self, value: Vector) -> LogisticRegression: ... + def setMaxIter(self, value: int) -> LogisticRegression: ... + def setRegParam(self, value: float) -> LogisticRegression: ... + def setTol(self, value: float) -> LogisticRegression: ... + def setElasticNetParam(self, value: float) -> LogisticRegression: ... + def setFitIntercept(self, value: bool) -> LogisticRegression: ... + def setStandardization(self, value: bool) -> LogisticRegression: ... + def setWeightCol(self, value: str) -> LogisticRegression: ... + def setAggregationDepth(self, value: int) -> LogisticRegression: ... class LogisticRegressionModel(JavaProbabilisticClassificationModel[Vector], _LogisticRegressionParams, JavaMLWritable, JavaMLReadable[LogisticRegressionModel], HasTrainingSummary[LogisticRegressionTrainingSummary]): @property @@ -156,6 +173,9 @@ class DecisionTreeClassifier(JavaProbabilisticClassifier[DecisionTreeClassificat def setMaxMemoryInMB(self, value: int) -> DecisionTreeClassifier: ... def setCacheNodeIds(self, value: bool) -> DecisionTreeClassifier: ... def setImpurity(self, value: str) -> DecisionTreeClassifier: ... + def setCheckpointInterval(self, value: int) -> DecisionTreeClassifier: ... + def setSeed(self, value: int) -> DecisionTreeClassifier: ... + def setWeightCol(self, value: str) -> DecisionTreeClassifier: ... class DecisionTreeClassificationModel(_DecisionTreeModel, JavaProbabilisticClassificationModel[Vector], _DecisionTreeClassifierParams, JavaMLWritable, JavaMLReadable[DecisionTreeClassificationModel]): @property @@ -176,6 +196,8 @@ class RandomForestClassifier(JavaProbabilisticClassifier[RandomForestClassificat def setNumTrees(self, value: int) -> RandomForestClassifier: ... def setSubsamplingRate(self, value: float) -> RandomForestClassifier: ... def setFeatureSubsetStrategy(self, value: str) -> RandomForestClassifier: ... + def setSeed(self, value: int) -> RandomForestClassifier: ... + def setCheckpointInterval(self, value: int) -> RandomForestClassifier: ... class RandomForestClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel[Vector], _RandomForestClassifierParams, JavaMLWritable, JavaMLReadable[RandomForestClassificationModel]): @property @@ -202,6 +224,10 @@ class GBTClassifier(JavaProbabilisticClassifier[GBTClassificationModel], _GBTCla def setSubsamplingRate(self, value: float) -> GBTClassifier: ... def setFeatureSubsetStrategy(self, value: str) -> GBTClassifier: ... def setValidationIndicatorCol(self, value: str) -> GBTClassifier: ... + def setMaxIter(self, value: int) -> GBTClassifier: ... + def setCheckpointInterval(self, value: int) -> GBTClassifier: ... + def setSeed(self, value: int) -> GBTClassifier: ... + def setStepSize(self, value: float) -> GBTClassifier: ... class GBTClassificationModel(_TreeEnsembleModel, JavaProbabilisticClassificationModel[Vector], _GBTClassifierParams, JavaMLWritable, JavaMLReadable[GBTClassificationModel]): @property @@ -221,6 +247,7 @@ class NaiveBayes(JavaProbabilisticClassifier[NaiveBayesModel], _NaiveBayesParams 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 setModelType(self, value: str) -> NaiveBayes: ... + def setWeightCol(self, value: str) -> NaiveBayes: ... class NaiveBayesModel(JavaProbabilisticClassificationModel[Vector], _NaiveBayesParams, JavaMLWritable, JavaMLReadable[NaiveBayesModel]): @property @@ -243,8 +270,12 @@ class MultilayerPerceptronClassifier(JavaProbabilisticClassifier[MultilayerPerce 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 setBlockSize(self, value: int) -> MultilayerPerceptronClassifier: ... - def setStepSize(self, value: float) -> MultilayerPerceptronClassifier: ... def setInitialWeights(self, value: Vector) -> MultilayerPerceptronClassifier: ... + def setMaxIter(self, value: int) -> MultilayerPerceptronClassifier: ... + def setSeed(self, value: int) -> MultilayerPerceptronClassifier: ... + def setTol(self, value: float) -> MultilayerPerceptronClassifier: ... + def setStepSize(self, value: float) -> MultilayerPerceptronClassifier: ... + def setSolver(self, value: str) -> MultilayerPerceptronClassifier: ... class MultilayerPerceptronClassificationModel(JavaProbabilisticClassificationModel[Vector], JavaMLWritable, JavaMLReadable[MultilayerPerceptronClassificationModel]): @property @@ -260,10 +291,18 @@ class OneVsRest(Estimator[OneVsRestModel], _OneVsRestParams, HasParallelism, Jav def __init__(self, *, featuresCol: str = ..., labelCol: str = ..., predictionCol: str = ..., rawPredictionCol: str = ..., classifier: Optional[Estimator[M]] = ..., weightCol: Optional[str] = ..., parallelism: int = ...) -> None: ... def setParams(self, *, featuresCol: Optional[str] = ..., labelCol: Optional[str] = ..., predictionCol: Optional[str] = ..., rawPredictionCol: str = ..., classifier: Optional[Estimator[M]] = ..., weightCol: Optional[str] = ..., parallelism: int = ...) -> OneVsRest: ... def setClassifier(self, value: Estimator[M]) -> OneVsRest: ... + def setLabelCol(self, value: str) -> OneVsRest: ... + def setFeaturesCol(self, value: str) -> OneVsRest: ... + def setPredictionCol(self, value: str) -> OneVsRest: ... + def setRawPredictionCol(self, value: str) -> OneVsRest: ... + def setWeightCol(self, value: str) -> OneVsRest: ... + def setParallelism(self, value: int) -> OneVsRest: ... def copy(self, extra: Optional[ParamMap] = ...) -> OneVsRest: ... class OneVsRestModel(Model, _OneVsRestParams, JavaMLReadable[OneVsRestModel], JavaMLWritable): models: List[Transformer] def __init__(self, models: List[Transformer]) -> None: ... - def setClassifier(self, value: Estimator[M]) -> OneVsRest: ... + def setFeaturesCol(self, value: str) -> OneVsRestModel: ... + def setPredictionCol(self, value: str) -> OneVsRestModel: ... + def setRawPredictionCol(self, value: str) -> OneVsRestModel: ... def copy(self, extra: Optional[ParamMap] = ...) -> OneVsRestModel: ... diff --git a/third_party/3/pyspark/ml/clustering.pyi b/third_party/3/pyspark/ml/clustering.pyi index f3b33277..2710d58d 100644 --- a/third_party/3/pyspark/ml/clustering.pyi +++ b/third_party/3/pyspark/ml/clustering.pyi @@ -32,6 +32,9 @@ class _GaussianMixtureParams(HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionC def getK(self) -> int: ... class GaussianMixtureModel(JavaModel, _GaussianMixtureParams, JavaMLWritable, JavaMLReadable[GaussianMixtureModel], HasTrainingSummary[GaussianMixtureSummary]): + def setFeaturesCol(self, value: str) -> GaussianMixtureModel: ... + def setPredictionCol(self, value: str) -> GaussianMixtureModel: ... + def setProbabilityCol(self, value: str) -> GaussianMixtureModel: ... @property def weights(self) -> List[float]: ... @property @@ -45,6 +48,14 @@ class GaussianMixture(JavaEstimator[GaussianMixtureModel], _GaussianMixtureParam def __init__(self, *, featuresCol: str = ..., predictionCol: str = ..., k: int = ..., probabilityCol: str = ..., tol: float = ..., maxIter: int = ..., seed: Optional[int] = ...) -> None: ... def setParams(self, *, featuresCol: str = ..., predictionCol: str = ..., k: int = ..., probabilityCol: str = ..., tol: float = ..., maxIter: int = ..., seed: Optional[int] = ...) -> GaussianMixture: ... def setK(self, value: int) -> GaussianMixture: ... + def setMaxIter(self, value: int) -> GaussianMixture: ... + def setFeaturesCol(self, value: str) -> GaussianMixture: ... + def setPredictionCol(self, value: str) -> GaussianMixture: ... + def setProbabilityCol(self, value: str) -> GaussianMixture: ... + def setWeightCol(self, value: str) -> GaussianMixture: ... + def setSeed(self, value: int) -> GaussianMixture: ... + def setTol(self, value: float) -> GaussianMixture: ... + def setAggregationDepth(self, value: int) -> GaussianMixture: ... class GaussianMixtureSummary(ClusteringSummary): @property @@ -66,6 +77,8 @@ class _KMeansParams(HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionCol, HasTo def getInitSteps(self) -> int: ... class KMeansModel(JavaModel, _KMeansParams, GeneralJavaMLWritable, JavaMLReadable[KMeansModel], HasTrainingSummary[KMeansSummary]): + def setFeaturesCol(self, value: str) -> KMeansModel: ... + def setPredictionCol(self, value: str) -> KMeansModel: ... def clusterCenters(self) -> List[ndarray]: ... @property def summary(self) -> KMeansSummary: ... @@ -78,6 +91,12 @@ class KMeans(JavaEstimator[KMeansModel], _KMeansParams, JavaMLWritable, JavaMLRe def setInitMode(self, value: str) -> KMeans: ... def setInitSteps(self, value: int) -> KMeans: ... def setDistanceMeasure(self, value: str) -> KMeans: ... + def setMaxIter(self, value: int) -> KMeans: ... + def setFeaturesCol(self, value: str) -> KMeans: ... + def setPredictionCol(self, value: str) -> KMeans: ... + def setSeed(self, value: int) -> KMeans: ... + def setTol(self, value: float) -> KMeans: ... + def setWeightCol(self, value: str) -> KMeans: ... class _BisectingKMeansParams(HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionCol, HasDistanceMeasure): k: Param[int] @@ -86,6 +105,8 @@ class _BisectingKMeansParams(HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionC def getMinDivisibleClusterSize(self) -> float: ... class BisectingKMeansModel(JavaModel, _BisectingKMeansParams, JavaMLWritable, JavaMLReadable[BisectingKMeansModel], HasTrainingSummary[BisectingKMeansSummary]): + def setFeaturesCol(self, value: str) -> BisectingKMeansModel: ... + def setPredictionCol(self, value: str) -> BisectingKMeansModel: ... def clusterCenters(self) -> List[ndarray]: ... def computeCost(self, dataset: DataFrame) -> float: ... @property @@ -98,6 +119,10 @@ class BisectingKMeans(JavaEstimator[BisectingKMeansModel], _BisectingKMeansParam def setK(self, value: int) -> BisectingKMeans: ... def setMinDivisibleClusterSize(self, value: float) -> BisectingKMeans: ... def setDistanceMeasure(self, value: str) -> BisectingKMeans: ... + def setMaxIter(self, value: int) -> BisectingKMeans: ... + def setFeaturesCol(self, value: str) -> BisectingKMeans: ... + def setPredictionCol(self, value: str) -> BisectingKMeans: ... + def setSeed(self, value: int) -> BisectingKMeans: ... class BisectingKMeansSummary(ClusteringSummary): @property @@ -126,6 +151,9 @@ class _LDAParams(HasMaxIter, HasFeaturesCol, HasSeed, HasCheckpointInterval): def getKeepLastCheckpoint(self) -> bool: ... class LDAModel(JavaModel, _LDAParams): + def setFeaturesCol(self, value: str) -> LDAModel: ... + def setSeed(self, value: int) -> LDAModel: ... + def setTopicDistributionCol(self, value: str) -> LDAModel: ... def isDistributed(self) -> bool: ... def vocabSize(self) -> int: ... def topicsMatrix(self) -> Matrix: ... @@ -145,6 +173,9 @@ class LocalLDAModel(LDAModel, JavaMLReadable[LocalLDAModel], JavaMLWritable): .. class LDA(JavaEstimator[LDAModel], _LDAParams, JavaMLReadable[LDA], JavaMLWritable): def __init__(self, *, featuresCol: str = ..., maxIter: int = ..., seed: Optional[int] = ..., checkpointInterval: int = ..., k: int = ..., optimizer: str = ..., learningOffset: float = ..., learningDecay: float = ..., subsamplingRate: float = ..., optimizeDocConcentration: bool = ..., docConcentration: Optional[List[float]] = ..., topicConcentration: Optional[float] = ..., topicDistributionCol: str = ..., keepLastCheckpoint: bool = ...) -> None: ... def setParams(self, *, featuresCol: str = ..., maxIter: int = ..., seed: Optional[int] = ..., checkpointInterval: int = ..., k: int = ..., optimizer: str = ..., learningOffset: float = ..., learningDecay: float = ..., subsamplingRate: float = ..., optimizeDocConcentration: bool = ..., docConcentration: Optional[List[float]] = ..., topicConcentration: Optional[float] = ..., topicDistributionCol: str = ..., keepLastCheckpoint: bool = ...) -> LDA: ... + def setCheckpointInterval(self, value: int) -> LDA: ... + def setSeed(self, value: int) -> LDA: ... + def setK(self, value: int) -> LDA: ... def setOptimizer(self, value: str) -> LDA: ... def setLearningOffset(self, value: float) -> LDA: ... def setLearningDecay(self, value: float) -> LDA: ... @@ -154,6 +185,8 @@ class LDA(JavaEstimator[LDAModel], _LDAParams, JavaMLReadable[LDA], JavaMLWritab def setTopicConcentration(self, value: float) -> LDA: ... def setTopicDistributionCol(self, value: str) -> LDA: ... def setKeepLastCheckpoint(self, value: bool) -> LDA: ... + def setMaxIter(self, value: int) -> LDA: ... + def setFeaturesCol(self, value: str) -> LDA: ... class _PowerIterationClusteringParams(HasMaxIter, HasWeightCol): k: Param[int] @@ -172,4 +205,6 @@ class PowerIterationClustering(_PowerIterationClusteringParams, JavaParams, Java def setInitMode(self, value: str) -> PowerIterationClustering: ... def setSrcCol(self, value: str) -> str: ... def setDstCol(self, value: str) -> PowerIterationClustering: ... + def setMaxIter(self, value: int) -> PowerIterationClustering: ... + def setWeightCol(self, value: str) -> PowerIterationClustering: ... def assignClusters(self, dataset: DataFrame) -> DataFrame: ... diff --git a/third_party/3/pyspark/ml/evaluation.pyi b/third_party/3/pyspark/ml/evaluation.pyi index e3ba13b2..38a68849 100644 --- a/third_party/3/pyspark/ml/evaluation.pyi +++ b/third_party/3/pyspark/ml/evaluation.pyi @@ -7,7 +7,7 @@ from pyspark.ml._typing import ParamMap, BinaryClassificationEvaluatorMetricType from pyspark.ml.wrapper import JavaParams from pyspark.ml.param import Param, Params -from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasRawPredictionCol, HasWeightCol +from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasWeightCol from pyspark.ml.util import JavaMLReadable, JavaMLWritable class Evaluator(Params): @@ -25,9 +25,12 @@ class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPrediction def __init__(self, *, rawPredictionCol: str = ..., labelCol: str = ..., metricName: BinaryClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., numBins: int = ...) -> None: ... def setMetricName(self, value: BinaryClassificationEvaluatorMetricType) -> BinaryClassificationEvaluator: ... def getMetricName(self) -> BinaryClassificationEvaluatorMetricType: ... - def setParams(self, *, rawPredictionCol: str = ..., labelCol: str = ..., metricName: BinaryClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., numBins: int = ...) -> BinaryClassificationEvaluator: ... def setNumBins(self, value: int) -> BinaryClassificationEvaluator: ... def getNumBins(self) -> int: ... + def setLabelCol(self, value: str) -> BinaryClassificationEvaluator: ... + def setRawPredictionCol(self, value: str) -> BinaryClassificationEvaluator: ... + def setWeightCol(self, value: str) -> BinaryClassificationEvaluator: ... +def setParams(self, *, rawPredictionCol: str = ..., labelCol: str = ..., metricName: BinaryClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., numBins: int = ...) -> BinaryClassificationEvaluator: ... class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeightCol, JavaMLReadable[RegressionEvaluator], JavaMLWritable): metricName: Param[RegressionEvaluatorMetricType] @@ -35,22 +38,32 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeigh def __init__(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: RegressionEvaluatorMetricType = ..., weightCol: Optional[str] = ..., throughOrigin: bool = ...) -> None: ... def setMetricName(self, value: RegressionEvaluatorMetricType) -> RegressionEvaluator: ... def getMetricName(self) -> RegressionEvaluatorMetricType: ... - def setParams(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: RegressionEvaluatorMetricType = ..., weightCol: Optional[str] = ..., throughOrigin: bool = ...) -> RegressionEvaluator: ... def setThroughOrigin(self, value: bool) -> RegressionEvaluator: ... def getThroughOrigin(self) -> bool: ... + def setLabelCol(self, value: str) -> RegressionEvaluator: ... + def setPredictionCol(self, value: str) -> RegressionEvaluator: ... + def setWeightCol(self, value: str) -> RegressionEvaluator: ... + def setParams(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: RegressionEvaluatorMetricType = ..., weightCol: Optional[str] = ..., throughOrigin: bool = ...) -> RegressionEvaluator: ... -class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeightCol, JavaMLReadable[MulticlassClassificationEvaluator], JavaMLWritable): +class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeightCol, HasProbabilityCol, JavaMLReadable[MulticlassClassificationEvaluator], JavaMLWritable): metricName: Param[MulticlassClassificationEvaluatorMetricType] metricLabel: Param[float] beta: Param[float] - def __init__(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: MulticlassClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., metricLabel: float = ..., beta: float = ...) -> None: ... + eps: Param[float] + def __init__(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: MulticlassClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., metricLabel: float = ..., beta: float = ..., probabilityCol: str = ..., eps: float = ...) -> None: ... def setMetricName(self, value: MulticlassClassificationEvaluatorMetricType) -> MulticlassClassificationEvaluator: ... def getMetricName(self) -> MulticlassClassificationEvaluatorMetricType: ... def setMetricLabel(self, value: float) -> MulticlassClassificationEvaluator: ... def getMetricLabel(self) -> float: ... def setBeta(self, value: float) -> MulticlassClassificationEvaluator: ... def getBeta(self) -> float: ... - def setParams(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: MulticlassClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., metricLabel: float = ..., beta: float = ...) -> MulticlassClassificationEvaluator: ... + def setEps(self, value: float) -> MulticlassClassificationEvaluator: ... + def getEps(self) -> float: ... + def setLabelCol(self, value: str) -> MulticlassClassificationEvaluator: ... + def setPredictionCol(self, value: str) -> MulticlassClassificationEvaluator: ... + def setProbabilityCol(self, value: str) -> MulticlassClassificationEvaluator: ... + def setWeightCol(self, value: str) -> MulticlassClassificationEvaluator: ... + def setParams(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: MulticlassClassificationEvaluatorMetricType = ..., weightCol: Optional[str] = ..., metricLabel: float = ..., beta: float = ..., probabilityCol: str = ..., eps: float = ...) -> MulticlassClassificationEvaluator: ... class MultilabelClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, JavaMLReadable[MultilabelClassificationEvaluator], JavaMLWritable): metricName: Param[MultilabelClassificationEvaluatorMetricType] @@ -60,17 +73,21 @@ class MultilabelClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictio def getMetricName(self) -> MultilabelClassificationEvaluatorMetricType: ... def setMetricLabel(self, value: float) -> MultilabelClassificationEvaluator: ... def getMetricLabel(self) -> float: ... + def setLabelCol(self, value: str) -> MultilabelClassificationEvaluator: ... + def setPredictionCol(self, value: str) -> MultilabelClassificationEvaluator: ... def setParams(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: MultilabelClassificationEvaluatorMetricType = ..., metricLabel: float = ...) -> MultilabelClassificationEvaluator: ... class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol, JavaMLReadable[ClusteringEvaluator], JavaMLWritable): metricName: Param[ClusteringEvaluatorMetricType] distanceMeasure: Param[str] def __init__(self, *, predictionCol: str = ..., featuresCol: str = ..., metricName: ClusteringEvaluatorMetricType = ..., distanceMeasure: str = ...) -> None: ... + def setParams(self, *, predictionCol: str = ..., featuresCol: str = ..., metricName: ClusteringEvaluatorMetricType = ..., distanceMeasure: str = ...) -> ClusteringEvaluator: ... def setMetricName(self, value: ClusteringEvaluatorMetricType) -> ClusteringEvaluator: ... def getMetricName(self) -> ClusteringEvaluatorMetricType: ... - def setParams(self, *, predictionCol: str = ..., featuresCol: str = ..., metricName: ClusteringEvaluatorMetricType = ..., distanceMeasure: str = ...) -> MulticlassClassificationEvaluator: ... - def setDistanceMeasure(self, value: str) -> MulticlassClassificationEvaluator: ... + def setDistanceMeasure(self, value: str) -> ClusteringEvaluator: ... def getDistanceMeasure(self) -> str: ... + def setFeaturesCol(self, value: str) -> ClusteringEvaluator: ... + def setPredictionCol(self, value: str) -> ClusteringEvaluator: ... class RankingEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, JavaMLReadable[RankingEvaluator], JavaMLWritable): metricName: Param[RankingEvaluatorMetricType] @@ -80,4 +97,6 @@ class RankingEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, JavaMLReada def getMetricName(self) -> RankingEvaluatorMetricType: ... def setK(self, value: int) -> RankingEvaluator: ... def getK(self) -> int: ... + def setLabelCol(self, value: str) -> RankingEvaluator: ... + def setPredictionCol(self, value: str) -> RankingEvaluator: ... def setParams(self, *, predictionCol: str = ..., labelCol: str = ..., metricName: RankingEvaluatorMetricType = ..., k: int = ...) -> RankingEvaluator: ...