# 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 *
[docs]@inherit_doc
class ConditionalKNN(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
conditionerCol (str): column holding identifiers for features that will be returned when queried (default: conditioner)
featuresCol (str): The name of the features column (default: features)
k (int): number of matches to return (default: 5)
labelCol (str): The name of the label column (default: labels)
leafSize (int): max size of the leaves of the tree (default: 50)
outputCol (str): The name of the output column (default: [self.uid]_output)
valuesCol (str): column holding values for each feature (key) that will be returned when queried (default: values)
"""
@keyword_only
def __init__(self, conditionerCol="conditioner", featuresCol="features", k=5, labelCol="labels", leafSize=50, outputCol=None, valuesCol="values"):
super(ConditionalKNN, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.nn.ConditionalKNN")
self.conditionerCol = Param(self, "conditionerCol", "conditionerCol: column holding identifiers for features that will be returned when queried (default: conditioner)")
self._setDefault(conditionerCol="conditioner")
self.featuresCol = Param(self, "featuresCol", "featuresCol: The name of the features column (default: features)")
self._setDefault(featuresCol="features")
self.k = Param(self, "k", "k: number of matches to return (default: 5)")
self._setDefault(k=5)
self.labelCol = Param(self, "labelCol", "labelCol: The name of the label column (default: labels)")
self._setDefault(labelCol="labels")
self.leafSize = Param(self, "leafSize", "leafSize: max size of the leaves of the tree (default: 50)")
self._setDefault(leafSize=50)
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column (default: [self.uid]_output)")
self._setDefault(outputCol=self.uid + "_output")
self.valuesCol = Param(self, "valuesCol", "valuesCol: column holding values for each feature (key) that will be returned when queried (default: values)")
self._setDefault(valuesCol="values")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, conditionerCol="conditioner", featuresCol="features", k=5, labelCol="labels", leafSize=50, outputCol=None, valuesCol="values"):
"""
Set the (keyword only) parameters
Args:
conditionerCol (str): column holding identifiers for features that will be returned when queried (default: conditioner)
featuresCol (str): The name of the features column (default: features)
k (int): number of matches to return (default: 5)
labelCol (str): The name of the label column (default: labels)
leafSize (int): max size of the leaves of the tree (default: 50)
outputCol (str): The name of the output column (default: [self.uid]_output)
valuesCol (str): column holding values for each feature (key) that will be returned when queried (default: values)
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def getConditionerCol(self):
"""
Returns:
str: column holding identifiers for features that will be returned when queried (default: conditioner)
"""
return self.getOrDefault(self.conditionerCol)
[docs] def getFeaturesCol(self):
"""
Returns:
str: The name of the features column (default: features)
"""
return self.getOrDefault(self.featuresCol)
[docs] def getK(self):
"""
Returns:
int: number of matches to return (default: 5)
"""
return self.getOrDefault(self.k)
[docs] def getLabelCol(self):
"""
Returns:
str: The name of the label column (default: labels)
"""
return self.getOrDefault(self.labelCol)
[docs] def getLeafSize(self):
"""
Returns:
int: max size of the leaves of the tree (default: 50)
"""
return self.getOrDefault(self.leafSize)
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column (default: [self.uid]_output)
"""
return self.getOrDefault(self.outputCol)
[docs] def getValuesCol(self):
"""
Returns:
str: column holding values for each feature (key) that will be returned when queried (default: values)
"""
return self.getOrDefault(self.valuesCol)
[docs] def setConditionerCol(self, value):
"""
Args:
conditionerCol: column holding identifiers for features that will be returned when queried (default: conditioner)
"""
self._set(conditionerCol=value)
return self
[docs] def setFeaturesCol(self, value):
"""
Args:
featuresCol: The name of the features column (default: features)
"""
self._set(featuresCol=value)
return self
[docs] def setK(self, value):
"""
Args:
k: number of matches to return (default: 5)
"""
self._set(k=value)
return self
[docs] def setLabelCol(self, value):
"""
Args:
labelCol: The name of the label column (default: labels)
"""
self._set(labelCol=value)
return self
[docs] def setLeafSize(self, value):
"""
Args:
leafSize: max size of the leaves of the tree (default: 50)
"""
self._set(leafSize=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column (default: [self.uid]_output)
"""
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 (default: values)
"""
self._set(valuesCol=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.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)
def _create_model(self, java_model):
return ConditionalKNNModel(java_model)
[docs]class ConditionalKNNModel(ComplexParamsMixin, JavaModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by :class:`ConditionalKNN`.
"""
[docs] def getBallTree(self):
"""
Returns:
object: the ballTree model used for perfoming queries
"""
return self.getOrDefault(self.ballTree)
[docs] def getConditionerCol(self):
"""
Returns:
str: column holding identifiers for features that will be returned when queried
"""
return self.getOrDefault(self.conditionerCol)
[docs] def getFeaturesCol(self):
"""
Returns:
str: The name of the features column
"""
return self.getOrDefault(self.featuresCol)
[docs] def getK(self):
"""
Returns:
int: number of matches to return
"""
return self.getOrDefault(self.k)
[docs] def getLabelCol(self):
"""
Returns:
str: The name of the label column
"""
return self.getOrDefault(self.labelCol)
[docs] def getLeafSize(self):
"""
Returns:
int: max size of the leaves of the tree
"""
return self.getOrDefault(self.leafSize)
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getValuesCol(self):
"""
Returns:
str: column holding values for each feature (key) that will be returned when queried
"""
return self.getOrDefault(self.valuesCol)
[docs] def setBallTree(self, value):
"""
Args:
ballTree: the ballTree model used for perfoming queries
"""
self._set(ballTree=value)
return self
[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] @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.nn.ConditionalKNNModel"
@staticmethod
def _from_java(java_stage):
module_name=ConditionalKNNModel.__module__
module_name=module_name.rsplit(".", 1)[0] + ".ConditionalKNNModel"
return from_java(java_stage, module_name)