# 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 pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from mmlspark.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from mmlspark.core.schema.Utils import *
from mmlspark.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
[docs]@inherit_doc
class RankingAdapter(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
itemCol (str): Column of items
k (int): number of items (default: 10)
labelCol (str): The name of the label column (default: label)
minRatingsPerItem (int): min ratings for items > 0 (default: 1)
minRatingsPerUser (int): min ratings for users > 0 (default: 1)
mode (str): recommendation mode (default: allUsers)
ratingCol (str): Column of ratings
recommender (object): estimator for selection
userCol (str): Column of users
"""
@keyword_only
def __init__(self, itemCol=None, k=10, labelCol="label", minRatingsPerItem=1, minRatingsPerUser=1, mode="allUsers", ratingCol=None, recommender=None, userCol=None):
super(RankingAdapter, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.recommendation.RankingAdapter")
self._cache = {}
self.itemCol = Param(self, "itemCol", "itemCol: Column of items")
self.k = Param(self, "k", "k: number of items (default: 10)")
self._setDefault(k=10)
self.labelCol = Param(self, "labelCol", "labelCol: The name of the label column (default: label)")
self._setDefault(labelCol="label")
self.minRatingsPerItem = Param(self, "minRatingsPerItem", "minRatingsPerItem: min ratings for items > 0 (default: 1)")
self._setDefault(minRatingsPerItem=1)
self.minRatingsPerUser = Param(self, "minRatingsPerUser", "minRatingsPerUser: min ratings for users > 0 (default: 1)")
self._setDefault(minRatingsPerUser=1)
self.mode = Param(self, "mode", "mode: recommendation mode (default: allUsers)")
self._setDefault(mode="allUsers")
self.ratingCol = Param(self, "ratingCol", "ratingCol: Column of ratings")
self.recommender = Param(self, "recommender", "recommender: estimator for selection", generateTypeConverter("recommender", self._cache, complexTypeConverter))
self.userCol = Param(self, "userCol", "userCol: Column of users")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[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
Args:
itemCol (str): Column of items
k (int): number of items (default: 10)
labelCol (str): The name of the label column (default: label)
minRatingsPerItem (int): min ratings for items > 0 (default: 1)
minRatingsPerUser (int): min ratings for users > 0 (default: 1)
mode (str): recommendation mode (default: allUsers)
ratingCol (str): Column of ratings
recommender (object): estimator for selection
userCol (str): Column of users
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def getItemCol(self):
"""
Returns:
str: Column of items
"""
return self.getOrDefault(self.itemCol)
[docs] def getK(self):
"""
Returns:
int: number of items (default: 10)
"""
return self.getOrDefault(self.k)
[docs] def getLabelCol(self):
"""
Returns:
str: The name of the label column (default: label)
"""
return self.getOrDefault(self.labelCol)
[docs] def getMinRatingsPerItem(self):
"""
Returns:
int: min ratings for items > 0 (default: 1)
"""
return self.getOrDefault(self.minRatingsPerItem)
[docs] def getMinRatingsPerUser(self):
"""
Returns:
int: min ratings for users > 0 (default: 1)
"""
return self.getOrDefault(self.minRatingsPerUser)
[docs] def getMode(self):
"""
Returns:
str: recommendation mode (default: allUsers)
"""
return self.getOrDefault(self.mode)
[docs] def getRatingCol(self):
"""
Returns:
str: Column of ratings
"""
return self.getOrDefault(self.ratingCol)
[docs] def getRecommender(self):
"""
Returns:
object: estimator for selection
"""
return self._cache.get("recommender", None)
[docs] def getUserCol(self):
"""
Returns:
str: Column of users
"""
return self.getOrDefault(self.userCol)
[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 (default: 10)
"""
self._set(k=value)
return self
[docs] def setLabelCol(self, value):
"""
Args:
labelCol: The name of the label column (default: label)
"""
self._set(labelCol=value)
return self
[docs] def setMinRatingsPerItem(self, value):
"""
Args:
minRatingsPerItem: min ratings for items > 0 (default: 1)
"""
self._set(minRatingsPerItem=value)
return self
[docs] def setMinRatingsPerUser(self, value):
"""
Args:
minRatingsPerUser: min ratings for users > 0 (default: 1)
"""
self._set(minRatingsPerUser=value)
return self
[docs] def setMode(self, value):
"""
Args:
mode: recommendation mode (default: allUsers)
"""
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] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.ml.spark.recommendation.RankingAdapter"
@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)
def _create_model(self, java_model):
return RankingAdapterModel(java_model)
[docs]class RankingAdapterModel(ComplexParamsMixin, JavaModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by :class:`RankingAdapter`.
"""
[docs] def getItemCol(self):
"""
Returns:
str: Column of items
"""
return self.getOrDefault(self.itemCol)
[docs] def getK(self):
"""
Returns:
int: number of items (default: 10)
"""
return self.getOrDefault(self.k)
[docs] def getLabelCol(self):
"""
Returns:
str: The name of the label column (default: label)
"""
return self.getOrDefault(self.labelCol)
[docs] def getMinRatingsPerItem(self):
"""
Returns:
int: min ratings for items > 0 (default: 1)
"""
return self.getOrDefault(self.minRatingsPerItem)
[docs] def getMinRatingsPerUser(self):
"""
Returns:
int: min ratings for users > 0 (default: 1)
"""
return self.getOrDefault(self.minRatingsPerUser)
[docs] def getMode(self):
"""
Returns:
str: recommendation mode (default: allUsers)
"""
return self.getOrDefault(self.mode)
[docs] def getRatingCol(self):
"""
Returns:
str: Column of ratings
"""
return self.getOrDefault(self.ratingCol)
[docs] def getRecommender(self):
"""
Returns:
object: estimator for selection
"""
return self._cache.get("recommender", None)
[docs] def getRecommenderModel(self):
"""
Returns:
object: recommenderModel
"""
return self._cache.get("recommenderModel", None)
[docs] def getUserCol(self):
"""
Returns:
str: Column of users
"""
return self.getOrDefault(self.userCol)
[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 (default: 10)
"""
self._set(k=value)
return self
[docs] def setLabelCol(self, value):
"""
Args:
labelCol: The name of the label column (default: label)
"""
self._set(labelCol=value)
return self
[docs] def setMinRatingsPerItem(self, value):
"""
Args:
minRatingsPerItem: min ratings for items > 0 (default: 1)
"""
self._set(minRatingsPerItem=value)
return self
[docs] def setMinRatingsPerUser(self, value):
"""
Args:
minRatingsPerUser: min ratings for users > 0 (default: 1)
"""
self._set(minRatingsPerUser=value)
return self
[docs] def setMode(self, value):
"""
Args:
mode: recommendation mode (default: allUsers)
"""
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 setRecommenderModel(self, value):
"""
Args:
recommenderModel: recommenderModel
"""
self._set(recommenderModel=value)
return self
[docs] def setUserCol(self, value):
"""
Args:
userCol: Column of users
"""
self._set(userCol=value)
return self
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.ml.spark.recommendation.RankingAdapterModel"
@staticmethod
def _from_java(java_stage):
module_name=RankingAdapterModel.__module__
module_name=module_name.rsplit(".", 1)[0] + ".RankingAdapterModel"
return from_java(java_stage, module_name)