Source code for

# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.

import sys
if sys.version >= '3':
    basestring = str

from pyspark import SparkContext, SQLContext
from pyspark.sql import DataFrame
from import *
from pyspark import keyword_only
from import JavaMLReadable, JavaMLWritable
from import running_on_synapse_internal
from import *
from import JavaTransformer, JavaEstimator, JavaModel
from import JavaEvaluator
from import inherit_doc
from import *
from import TypeConverters
from import generateTypeConverter, complexTypeConverter
from import RecommendationIndexerModel

[docs]@inherit_doc class RecommendationIndexer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: itemInputCol (str): Item Input Col itemOutputCol (str): Item Output Col ratingCol (str): Rating Col userInputCol (str): User Input Col userOutputCol (str): User Output Col """ itemInputCol = Param(Params._dummy(), "itemInputCol", "Item Input Col", typeConverter=TypeConverters.toString) itemOutputCol = Param(Params._dummy(), "itemOutputCol", "Item Output Col", typeConverter=TypeConverters.toString) ratingCol = Param(Params._dummy(), "ratingCol", "Rating Col", typeConverter=TypeConverters.toString) userInputCol = Param(Params._dummy(), "userInputCol", "User Input Col", typeConverter=TypeConverters.toString) userOutputCol = Param(Params._dummy(), "userOutputCol", "User Output Col", typeConverter=TypeConverters.toString) @keyword_only def __init__( self, java_obj=None, itemInputCol=None, itemOutputCol=None, ratingCol=None, userInputCol=None, userOutputCol=None ): super(RecommendationIndexer, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("", self.uid) else: self._java_obj = java_obj if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs if java_obj is None: for k,v in kwargs.items(): if v is not None: getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only def setParams( self, itemInputCol=None, itemOutputCol=None, ratingCol=None, userInputCol=None, userOutputCol=None ): """ Set the (keyword only) parameters """ if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs return self._set(**kwargs)
[docs] @classmethod def read(cls): """ Returns an MLReader instance for this class. """ return JavaMMLReader(cls)
[docs] @staticmethod def getJavaPackage(): """ Returns package name String. """ return ""
@staticmethod def _from_java(java_stage): module_name=RecommendationIndexer.__module__ module_name=module_name.rsplit(".", 1)[0] + ".RecommendationIndexer" return from_java(java_stage, module_name)
[docs] def setItemInputCol(self, value): """ Args: itemInputCol: Item Input Col """ self._set(itemInputCol=value) return self
[docs] def setItemOutputCol(self, value): """ Args: itemOutputCol: Item Output Col """ self._set(itemOutputCol=value) return self
[docs] def setRatingCol(self, value): """ Args: ratingCol: Rating Col """ self._set(ratingCol=value) return self
[docs] def setUserInputCol(self, value): """ Args: userInputCol: User Input Col """ self._set(userInputCol=value) return self
[docs] def setUserOutputCol(self, value): """ Args: userOutputCol: User Output Col """ self._set(userOutputCol=value) return self
[docs] def getItemInputCol(self): """ Returns: itemInputCol: Item Input Col """ return self.getOrDefault(self.itemInputCol)
[docs] def getItemOutputCol(self): """ Returns: itemOutputCol: Item Output Col """ return self.getOrDefault(self.itemOutputCol)
[docs] def getRatingCol(self): """ Returns: ratingCol: Rating Col """ return self.getOrDefault(self.ratingCol)
[docs] def getUserInputCol(self): """ Returns: userInputCol: User Input Col """ return self.getOrDefault(self.userInputCol)
[docs] def getUserOutputCol(self): """ Returns: userOutputCol: User Output Col """ return self.getOrDefault(self.userOutputCol)
def _create_model(self, java_model): try: model = RecommendationIndexerModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = RecommendationIndexerModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)