# 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 synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
from synapse.ml.recommendation.RecommendationIndexerModel import RecommendationIndexerModel
[docs]@inherit_doc
class RecommendationIndexer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
itemInputCol (object): Item Input Col
itemOutputCol (object): Item Output Col
ratingCol (object): Rating Col
userInputCol (object): User Input Col
userOutputCol (object): User Output Col
"""
itemInputCol = Param(Params._dummy(), "itemInputCol", "Item Input Col")
itemOutputCol = Param(Params._dummy(), "itemOutputCol", "Item Output Col")
ratingCol = Param(Params._dummy(), "ratingCol", "Rating Col")
userInputCol = Param(Params._dummy(), "userInputCol", "User Input Col")
userOutputCol = Param(Params._dummy(), "userOutputCol", "User Output Col")
@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("com.microsoft.azure.synapse.ml.recommendation.RecommendationIndexer", 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 "com.microsoft.azure.synapse.ml.recommendation.RecommendationIndexer"
@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 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 setUserOutputCol(self, value):
"""
Args:
userOutputCol: User Output Col
"""
self._set(userOutputCol=value)
return self
[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 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)