# 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.platform import running_on_synapse_internal
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.nn.ConditionalKNNModel import ConditionalKNNModel
[docs]@inherit_doc
class ConditionalKNN(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
conditionerCol (str): column holding identifiers for features that will be returned when queried
featuresCol (str): The name of the features column
k (int): number of matches to return
labelCol (str): The name of the label column
leafSize (int): max size of the leaves of the tree
outputCol (str): The name of the output column
valuesCol (str): column holding values for each feature (key) that will be returned when queried
"""
conditionerCol = Param(Params._dummy(), "conditionerCol", "column holding identifiers for features that will be returned when queried", typeConverter=TypeConverters.toString)
featuresCol = Param(Params._dummy(), "featuresCol", "The name of the features column", typeConverter=TypeConverters.toString)
k = Param(Params._dummy(), "k", "number of matches to return", typeConverter=TypeConverters.toInt)
labelCol = Param(Params._dummy(), "labelCol", "The name of the label column", typeConverter=TypeConverters.toString)
leafSize = Param(Params._dummy(), "leafSize", "max size of the leaves of the tree", typeConverter=TypeConverters.toInt)
outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString)
valuesCol = Param(Params._dummy(), "valuesCol", "column holding values for each feature (key) that will be returned when queried", typeConverter=TypeConverters.toString)
@keyword_only
def __init__(
self,
java_obj=None,
conditionerCol="conditioner",
featuresCol="features",
k=5,
labelCol="labels",
leafSize=50,
outputCol="ConditionalKNN_8a160eec8f7e_output",
valuesCol="values"
):
super(ConditionalKNN, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.nn.ConditionalKNN", self.uid)
else:
self._java_obj = java_obj
self._setDefault(conditionerCol="conditioner")
self._setDefault(featuresCol="features")
self._setDefault(k=5)
self._setDefault(labelCol="labels")
self._setDefault(leafSize=50)
self._setDefault(outputCol="ConditionalKNN_8a160eec8f7e_output")
self._setDefault(valuesCol="values")
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,
conditionerCol="conditioner",
featuresCol="features",
k=5,
labelCol="labels",
leafSize=50,
outputCol="ConditionalKNN_8a160eec8f7e_output",
valuesCol="values"
):
"""
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.nn.ConditionalKNN"
@staticmethod
def _from_java(java_stage):
module_name=ConditionalKNN.__module__
module_name=module_name.rsplit(".", 1)[0] + ".ConditionalKNN"
return from_java(java_stage, module_name)
[docs] def setConditionerCol(self, value):
"""
Args:
conditionerCol: column holding identifiers for features that will be returned when queried
"""
self._set(conditionerCol=value)
return self
[docs] def setFeaturesCol(self, value):
"""
Args:
featuresCol: The name of the features column
"""
self._set(featuresCol=value)
return self
[docs] def setK(self, value):
"""
Args:
k: number of matches to return
"""
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 setLeafSize(self, value):
"""
Args:
leafSize: max size of the leaves of the tree
"""
self._set(leafSize=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def setValuesCol(self, value):
"""
Args:
valuesCol: column holding values for each feature (key) that will be returned when queried
"""
self._set(valuesCol=value)
return self
[docs] def getConditionerCol(self):
"""
Returns:
conditionerCol: column holding identifiers for features that will be returned when queried
"""
return self.getOrDefault(self.conditionerCol)
[docs] def getFeaturesCol(self):
"""
Returns:
featuresCol: The name of the features column
"""
return self.getOrDefault(self.featuresCol)
[docs] def getK(self):
"""
Returns:
k: number of matches to return
"""
return self.getOrDefault(self.k)
[docs] def getLabelCol(self):
"""
Returns:
labelCol: The name of the label column
"""
return self.getOrDefault(self.labelCol)
[docs] def getLeafSize(self):
"""
Returns:
leafSize: max size of the leaves of the tree
"""
return self.getOrDefault(self.leafSize)
[docs] def getOutputCol(self):
"""
Returns:
outputCol: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getValuesCol(self):
"""
Returns:
valuesCol: column holding values for each feature (key) that will be returned when queried
"""
return self.getOrDefault(self.valuesCol)
def _create_model(self, java_model):
try:
model = ConditionalKNNModel(java_obj=java_model)
model._transfer_params_from_java()
except TypeError:
model = ConditionalKNNModel._from_java(java_model)
return model
def _fit(self, dataset):
java_model = self._fit_java(dataset)
return self._create_model(java_model)