# 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.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 VowpalWabbitRegressor(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
additionalFeatures (list): Additional feature columns (default: [Ljava.lang.String;@27629b46)
args (str): VW command line arguments passed (default: )
featuresCol (str): features column name (default: features)
hashSeed (int): Seed used for hashing (default: 0)
ignoreNamespaces (str): Namespaces to be ignored (first letter only)
initialModel (list): Initial model to start from
interactions (list): Interaction terms as specified by -q
l1 (double): l_1 lambda
l2 (double): l_2 lambda
labelCol (str): label column name (default: label)
learningRate (double): Learning rate
numBits (int): Number of bits used (default: 18)
numPasses (int): Number of passes over the data (default: 1)
powerT (double): t power value
predictionCol (str): prediction column name (default: prediction)
weightCol (str): The name of the weight column
"""
@keyword_only
def __init__(self, additionalFeatures=[], args="", featuresCol="features", hashSeed=0, ignoreNamespaces=None, initialModel=None, interactions=None, l1=None, l2=None, labelCol="label", learningRate=None, numBits=18, numPasses=1, powerT=None, predictionCol="prediction", weightCol=None):
super(VowpalWabbitRegressor, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.vw.VowpalWabbitRegressor")
self.additionalFeatures = Param(self, "additionalFeatures", "additionalFeatures: Additional feature columns (default: [Ljava.lang.String;@27629b46)")
self._setDefault(additionalFeatures=[])
self.args = Param(self, "args", "args: VW command line arguments passed (default: )")
self._setDefault(args="")
self.featuresCol = Param(self, "featuresCol", "featuresCol: features column name (default: features)")
self._setDefault(featuresCol="features")
self.hashSeed = Param(self, "hashSeed", "hashSeed: Seed used for hashing (default: 0)")
self._setDefault(hashSeed=0)
self.ignoreNamespaces = Param(self, "ignoreNamespaces", "ignoreNamespaces: Namespaces to be ignored (first letter only)")
self.initialModel = Param(self, "initialModel", "initialModel: Initial model to start from")
self.interactions = Param(self, "interactions", "interactions: Interaction terms as specified by -q")
self.l1 = Param(self, "l1", "l1: l_1 lambda")
self.l2 = Param(self, "l2", "l2: l_2 lambda")
self.labelCol = Param(self, "labelCol", "labelCol: label column name (default: label)")
self._setDefault(labelCol="label")
self.learningRate = Param(self, "learningRate", "learningRate: Learning rate")
self.numBits = Param(self, "numBits", "numBits: Number of bits used (default: 18)")
self._setDefault(numBits=18)
self.numPasses = Param(self, "numPasses", "numPasses: Number of passes over the data (default: 1)")
self._setDefault(numPasses=1)
self.powerT = Param(self, "powerT", "powerT: t power value")
self.predictionCol = Param(self, "predictionCol", "predictionCol: prediction column name (default: prediction)")
self._setDefault(predictionCol="prediction")
self.weightCol = Param(self, "weightCol", "weightCol: The name of the weight column")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, additionalFeatures=[], args="", featuresCol="features", hashSeed=0, ignoreNamespaces=None, initialModel=None, interactions=None, l1=None, l2=None, labelCol="label", learningRate=None, numBits=18, numPasses=1, powerT=None, predictionCol="prediction", weightCol=None):
"""
Set the (keyword only) parameters
Args:
additionalFeatures (list): Additional feature columns (default: [Ljava.lang.String;@27629b46)
args (str): VW command line arguments passed (default: )
featuresCol (str): features column name (default: features)
hashSeed (int): Seed used for hashing (default: 0)
ignoreNamespaces (str): Namespaces to be ignored (first letter only)
initialModel (list): Initial model to start from
interactions (list): Interaction terms as specified by -q
l1 (double): l_1 lambda
l2 (double): l_2 lambda
labelCol (str): label column name (default: label)
learningRate (double): Learning rate
numBits (int): Number of bits used (default: 18)
numPasses (int): Number of passes over the data (default: 1)
powerT (double): t power value
predictionCol (str): prediction column name (default: prediction)
weightCol (str): The name of the weight column
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setAdditionalFeatures(self, value):
"""
Args:
additionalFeatures (list): Additional feature columns (default: [Ljava.lang.String;@27629b46)
"""
self._set(additionalFeatures=value)
return self
[docs] def getAdditionalFeatures(self):
"""
Returns:
list: Additional feature columns (default: [Ljava.lang.String;@27629b46)
"""
return self.getOrDefault(self.additionalFeatures)
[docs] def setArgs(self, value):
"""
Args:
args (str): VW command line arguments passed (default: )
"""
self._set(args=value)
return self
[docs] def getArgs(self):
"""
Returns:
str: VW command line arguments passed (default: )
"""
return self.getOrDefault(self.args)
[docs] def setFeaturesCol(self, value):
"""
Args:
featuresCol (str): features column name (default: features)
"""
self._set(featuresCol=value)
return self
[docs] def getFeaturesCol(self):
"""
Returns:
str: features column name (default: features)
"""
return self.getOrDefault(self.featuresCol)
[docs] def setHashSeed(self, value):
"""
Args:
hashSeed (int): Seed used for hashing (default: 0)
"""
self._set(hashSeed=value)
return self
[docs] def getHashSeed(self):
"""
Returns:
int: Seed used for hashing (default: 0)
"""
return self.getOrDefault(self.hashSeed)
[docs] def setIgnoreNamespaces(self, value):
"""
Args:
ignoreNamespaces (str): Namespaces to be ignored (first letter only)
"""
self._set(ignoreNamespaces=value)
return self
[docs] def getIgnoreNamespaces(self):
"""
Returns:
str: Namespaces to be ignored (first letter only)
"""
return self.getOrDefault(self.ignoreNamespaces)
[docs] def setInitialModel(self, value):
"""
Args:
initialModel (list): Initial model to start from
"""
self._set(initialModel=value)
return self
[docs] def getInitialModel(self):
"""
Returns:
list: Initial model to start from
"""
return self.getOrDefault(self.initialModel)
[docs] def setInteractions(self, value):
"""
Args:
interactions (list): Interaction terms as specified by -q
"""
self._set(interactions=value)
return self
[docs] def getInteractions(self):
"""
Returns:
list: Interaction terms as specified by -q
"""
return self.getOrDefault(self.interactions)
[docs] def setL1(self, value):
"""
Args:
l1 (double): l_1 lambda
"""
self._set(l1=value)
return self
[docs] def getL1(self):
"""
Returns:
double: l_1 lambda
"""
return self.getOrDefault(self.l1)
[docs] def setL2(self, value):
"""
Args:
l2 (double): l_2 lambda
"""
self._set(l2=value)
return self
[docs] def getL2(self):
"""
Returns:
double: l_2 lambda
"""
return self.getOrDefault(self.l2)
[docs] def setLabelCol(self, value):
"""
Args:
labelCol (str): label column name (default: label)
"""
self._set(labelCol=value)
return self
[docs] def getLabelCol(self):
"""
Returns:
str: label column name (default: label)
"""
return self.getOrDefault(self.labelCol)
[docs] def setLearningRate(self, value):
"""
Args:
learningRate (double): Learning rate
"""
self._set(learningRate=value)
return self
[docs] def getLearningRate(self):
"""
Returns:
double: Learning rate
"""
return self.getOrDefault(self.learningRate)
[docs] def setNumBits(self, value):
"""
Args:
numBits (int): Number of bits used (default: 18)
"""
self._set(numBits=value)
return self
[docs] def getNumBits(self):
"""
Returns:
int: Number of bits used (default: 18)
"""
return self.getOrDefault(self.numBits)
[docs] def setNumPasses(self, value):
"""
Args:
numPasses (int): Number of passes over the data (default: 1)
"""
self._set(numPasses=value)
return self
[docs] def getNumPasses(self):
"""
Returns:
int: Number of passes over the data (default: 1)
"""
return self.getOrDefault(self.numPasses)
[docs] def setPowerT(self, value):
"""
Args:
powerT (double): t power value
"""
self._set(powerT=value)
return self
[docs] def getPowerT(self):
"""
Returns:
double: t power value
"""
return self.getOrDefault(self.powerT)
[docs] def setPredictionCol(self, value):
"""
Args:
predictionCol (str): prediction column name (default: prediction)
"""
self._set(predictionCol=value)
return self
[docs] def getPredictionCol(self):
"""
Returns:
str: prediction column name (default: prediction)
"""
return self.getOrDefault(self.predictionCol)
[docs] def setWeightCol(self, value):
"""
Args:
weightCol (str): The name of the weight column
"""
self._set(weightCol=value)
return self
[docs] def getWeightCol(self):
"""
Returns:
str: The name of the weight column
"""
return self.getOrDefault(self.weightCol)
[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.vw.VowpalWabbitRegressor"
@staticmethod
def _from_java(java_stage):
module_name=VowpalWabbitRegressor.__module__
module_name=module_name.rsplit(".", 1)[0] + ".VowpalWabbitRegressor"
return from_java(java_stage, module_name)
def _create_model(self, java_model):
return VowpalWabbitRegressorModel(java_model)
[docs]class VowpalWabbitRegressorModel(ComplexParamsMixin, JavaModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by :class:`VowpalWabbitRegressor`.
This class is left empty on purpose.
All necessary methods are exposed through inheritance.
"""
[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.vw.VowpalWabbitRegressorModel"
@staticmethod
def _from_java(java_stage):
module_name=VowpalWabbitRegressorModel.__module__
module_name=module_name.rsplit(".", 1)[0] + ".VowpalWabbitRegressorModel"
return from_java(java_stage, module_name)