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 RankingAdapterModel

[docs]@inherit_doc class RankingAdapter(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: itemCol (str): Column of items k (int): number of items labelCol (str): The name of the label column minRatingsPerItem (int): min ratings for items > 0 minRatingsPerUser (int): min ratings for users > 0 mode (str): recommendation mode ratingCol (str): Column of ratings recommender (object): estimator for selection userCol (str): Column of users """ itemCol = Param(Params._dummy(), "itemCol", "Column of items", typeConverter=TypeConverters.toString) k = Param(Params._dummy(), "k", "number of items", typeConverter=TypeConverters.toInt) labelCol = Param(Params._dummy(), "labelCol", "The name of the label column", typeConverter=TypeConverters.toString) minRatingsPerItem = Param(Params._dummy(), "minRatingsPerItem", "min ratings for items > 0", typeConverter=TypeConverters.toInt) minRatingsPerUser = Param(Params._dummy(), "minRatingsPerUser", "min ratings for users > 0", typeConverter=TypeConverters.toInt) mode = Param(Params._dummy(), "mode", "recommendation mode", typeConverter=TypeConverters.toString) ratingCol = Param(Params._dummy(), "ratingCol", "Column of ratings", typeConverter=TypeConverters.toString) recommender = Param(Params._dummy(), "recommender", "estimator for selection") userCol = Param(Params._dummy(), "userCol", "Column of users", typeConverter=TypeConverters.toString) @keyword_only def __init__( self, java_obj=None, itemCol=None, k=10, labelCol="label", minRatingsPerItem=1, minRatingsPerUser=1, mode="allUsers", ratingCol=None, recommender=None, userCol=None ): super(RankingAdapter, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("", self.uid) else: self._java_obj = java_obj self._setDefault(k=10) self._setDefault(labelCol="label") self._setDefault(minRatingsPerItem=1) self._setDefault(minRatingsPerUser=1) self._setDefault(mode="allUsers") 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, itemCol=None, k=10, labelCol="label", minRatingsPerItem=1, minRatingsPerUser=1, mode="allUsers", ratingCol=None, recommender=None, userCol=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=RankingAdapter.__module__ module_name=module_name.rsplit(".", 1)[0] + ".RankingAdapter" return from_java(java_stage, module_name)
[docs] def setItemCol(self, value): """ Args: itemCol: Column of items """ self._set(itemCol=value) return self
[docs] def setK(self, value): """ Args: k: number of items """ self._set(k=value) return self
[docs] def setLabelCol(self, value): """ Args: labelCol: The name of the label column """ self._set(labelCol=value) return self
[docs] def setMinRatingsPerItem(self, value): """ Args: minRatingsPerItem: min ratings for items > 0 """ self._set(minRatingsPerItem=value) return self
[docs] def setMinRatingsPerUser(self, value): """ Args: minRatingsPerUser: min ratings for users > 0 """ self._set(minRatingsPerUser=value) return self
[docs] def setMode(self, value): """ Args: mode: recommendation mode """ self._set(mode=value) return self
[docs] def setRatingCol(self, value): """ Args: ratingCol: Column of ratings """ self._set(ratingCol=value) return self
[docs] def setRecommender(self, value): """ Args: recommender: estimator for selection """ self._set(recommender=value) return self
[docs] def setUserCol(self, value): """ Args: userCol: Column of users """ self._set(userCol=value) return self
[docs] def getItemCol(self): """ Returns: itemCol: Column of items """ return self.getOrDefault(self.itemCol)
[docs] def getK(self): """ Returns: k: number of items """ return self.getOrDefault(self.k)
[docs] def getLabelCol(self): """ Returns: labelCol: The name of the label column """ return self.getOrDefault(self.labelCol)
[docs] def getMinRatingsPerItem(self): """ Returns: minRatingsPerItem: min ratings for items > 0 """ return self.getOrDefault(self.minRatingsPerItem)
[docs] def getMinRatingsPerUser(self): """ Returns: minRatingsPerUser: min ratings for users > 0 """ return self.getOrDefault(self.minRatingsPerUser)
[docs] def getMode(self): """ Returns: mode: recommendation mode """ return self.getOrDefault(self.mode)
[docs] def getRatingCol(self): """ Returns: ratingCol: Column of ratings """ return self.getOrDefault(self.ratingCol)
[docs] def getRecommender(self): """ Returns: recommender: estimator for selection """ return JavaParams._from_java(self._java_obj.getRecommender())
[docs] def getUserCol(self): """ Returns: userCol: Column of users """ return self.getOrDefault(self.userCol)
def _create_model(self, java_model): try: model = RankingAdapterModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = RankingAdapterModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)