SPARK-29360

third_party/3/pyspark/ml/fpm.pyi

@@ -9,35 +9,31 @@ from pyspark.ml.wrapper import JavaEstimator, JavaParams, JavaModel
 from pyspark.ml.param.shared import *
 from pyspark.sql.dataframe import DataFrame
 
-class HasMinSupport(Params):
+class _FPGrowthParams(HasPredictionCol):
+    itemsCol: Param[str]
     minSupport: Param[float]
-    def setMinSupport(self: P, value: float) -> P: ...
-    def getMinSupport(self) -> float: ...
-
-class HasNumPartitions(Params):
     numPartitions: Param[int]
-    def setNumPartitions(self: P, value: int) -> P: ...
-    def getNumPartitions(self) -> int: ...
-
-class HasMinConfidence(Params):
     minConfidence: Param[float]
-    def setMinConfidence(self: P, value: float) -> P: ...
-    def getMinConfidence(self) -> float: ...
-
-class HasItemsCol(Params):
-    itemsCol: Param[str]
-    def setItemsCol(self: P, value: str) -> P: ...
     def getItemsCol(self) -> str: ...
+    def getMinSupport(self) -> float: ...
+    def getNumPartitions(self) -> int: ...
+    def getMinConfidence(self) -> float: ...
 
-class FPGrowthModel(JavaModel, JavaMLWritable, JavaMLReadable[FPGrowthModel]):
+class FPGrowthModel(JavaModel, _FPGrowthParams, JavaMLWritable, JavaMLReadable[FPGrowthModel]):
+    def setItemsCol(self, value: str) -> FPGrowthModel: ...
+    def setMinConfidence(self, value: float) -> FPGrowthModel: ...
     @property
     def freqItemsets(self) -> DataFrame: ...
     @property
     def associationRules(self) -> DataFrame: ...
 
-class FPGrowth(JavaEstimator[FPGrowthModel], HasItemsCol, HasPredictionCol, HasMinSupport, HasNumPartitions, HasMinConfidence, JavaMLWritable, JavaMLReadable[FPGrowth]):
+class FPGrowth(JavaEstimator[FPGrowthModel], _FPGrowthParams, JavaMLWritable, JavaMLReadable[FPGrowth]):
     def __init__(self, *, minSupport: float = ..., minConfidence: float = ..., itemsCol: str = ..., predictionCol: str = ..., numPartitions: Optional[int] = ...) -> None: ...
     def setParams(self, *, minSupport: float = ..., minConfidence: float = ..., itemsCol: str = ..., predictionCol: str = ..., numPartitions: Optional[int] = ...) -> FPGrowth: ...
+    def setItemsCol(self, value: str) -> FPGrowth: ...
+    def setMinSupport(self, value: float) -> FPGrowth: ...
+    def setNumPartitions(self, value: int) -> FPGrowth: ...
+    def setMinConfidence(self, value: float) -> FPGrowth: ...
 
 class PrefixSpan(JavaParams):
     minSupport: Param[float]
@@ -46,4 +42,12 @@ class PrefixSpan(JavaParams):
     sequenceCol: Param[str]
     def __init__(self, *, minSupport: float = ..., maxPatternLength: int = ..., maxLocalProjDBSize: int = ..., sequenceCol: str = ...) -> None: ...
     def setParams(self, *, minSupport: float = ..., maxPatternLength: int = ..., maxLocalProjDBSize: int = ..., sequenceCol: str = ...) -> PrefixSpan: ...
+    def setMinSupport(self, value: float) -> PrefixSpan: ...
+    def getMinSupport(self) -> float: ...
+    def setMaxPatternLength(self, value: int) -> PrefixSpan: ...
+    def getMaxPatternLength(self) -> int: ...
+    def setMaxLocalProjDBSize(self, value: int) -> PrefixSpan: ...
+    def getMaxLocalProjDBSize(self) -> int: ...
+    def setSequenceCol(self, value: str) -> PrefixSpan: ...
+    def getSequenceCol(self) -> str: ...
     def findFrequentSequentialPatterns(self, dataset: DataFrame) -> DataFrame: ...