Source code for mmlspark.nn.ConditionalKNN

# 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)