# 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
[docs]@inherit_doc
class VowpalWabbitFeaturizer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
inputCols (list): The names of the input columns
numBits (int): Number of bits used to mask
outputCol (str): The name of the output column
prefixStringsWithColumnName (bool): Prefix string features with column name
preserveOrderNumBits (int): Number of bits used to preserve the feature order. This will reduce the hash size. Needs to be large enough to fit count the maximum number of words
seed (int): Hash seed
stringSplitInputCols (list): Input cols that should be split at word boundaries
sumCollisions (bool): Sums collisions if true, otherwise removes them
"""
inputCols = Param(Params._dummy(), "inputCols", "The names of the input columns", typeConverter=TypeConverters.toListString)
numBits = Param(Params._dummy(), "numBits", "Number of bits used to mask", typeConverter=TypeConverters.toInt)
outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString)
prefixStringsWithColumnName = Param(Params._dummy(), "prefixStringsWithColumnName", "Prefix string features with column name", typeConverter=TypeConverters.toBoolean)
preserveOrderNumBits = Param(Params._dummy(), "preserveOrderNumBits", "Number of bits used to preserve the feature order. This will reduce the hash size. Needs to be large enough to fit count the maximum number of words", typeConverter=TypeConverters.toInt)
seed = Param(Params._dummy(), "seed", "Hash seed", typeConverter=TypeConverters.toInt)
stringSplitInputCols = Param(Params._dummy(), "stringSplitInputCols", "Input cols that should be split at word boundaries", typeConverter=TypeConverters.toListString)
sumCollisions = Param(Params._dummy(), "sumCollisions", "Sums collisions if true, otherwise removes them", typeConverter=TypeConverters.toBoolean)
@keyword_only
def __init__(
self,
java_obj=None,
inputCols=[],
numBits=30,
outputCol="features",
prefixStringsWithColumnName=True,
preserveOrderNumBits=0,
seed=0,
stringSplitInputCols=[],
sumCollisions=True
):
super(VowpalWabbitFeaturizer, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.vw.VowpalWabbitFeaturizer", self.uid)
else:
self._java_obj = java_obj
self._setDefault(inputCols=[])
self._setDefault(numBits=30)
self._setDefault(outputCol="features")
self._setDefault(prefixStringsWithColumnName=True)
self._setDefault(preserveOrderNumBits=0)
self._setDefault(seed=0)
self._setDefault(stringSplitInputCols=[])
self._setDefault(sumCollisions=True)
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,
inputCols=[],
numBits=30,
outputCol="features",
prefixStringsWithColumnName=True,
preserveOrderNumBits=0,
seed=0,
stringSplitInputCols=[],
sumCollisions=True
):
"""
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.vw.VowpalWabbitFeaturizer"
@staticmethod
def _from_java(java_stage):
module_name=VowpalWabbitFeaturizer.__module__
module_name=module_name.rsplit(".", 1)[0] + ".VowpalWabbitFeaturizer"
return from_java(java_stage, module_name)
[docs] def setNumBits(self, value):
"""
Args:
numBits: Number of bits used to mask
"""
self._set(numBits=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def setPrefixStringsWithColumnName(self, value):
"""
Args:
prefixStringsWithColumnName: Prefix string features with column name
"""
self._set(prefixStringsWithColumnName=value)
return self
[docs] def setPreserveOrderNumBits(self, value):
"""
Args:
preserveOrderNumBits: Number of bits used to preserve the feature order. This will reduce the hash size. Needs to be large enough to fit count the maximum number of words
"""
self._set(preserveOrderNumBits=value)
return self
[docs] def setSeed(self, value):
"""
Args:
seed: Hash seed
"""
self._set(seed=value)
return self
[docs] def setSumCollisions(self, value):
"""
Args:
sumCollisions: Sums collisions if true, otherwise removes them
"""
self._set(sumCollisions=value)
return self
[docs] def getNumBits(self):
"""
Returns:
numBits: Number of bits used to mask
"""
return self.getOrDefault(self.numBits)
[docs] def getOutputCol(self):
"""
Returns:
outputCol: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getPrefixStringsWithColumnName(self):
"""
Returns:
prefixStringsWithColumnName: Prefix string features with column name
"""
return self.getOrDefault(self.prefixStringsWithColumnName)
[docs] def getPreserveOrderNumBits(self):
"""
Returns:
preserveOrderNumBits: Number of bits used to preserve the feature order. This will reduce the hash size. Needs to be large enough to fit count the maximum number of words
"""
return self.getOrDefault(self.preserveOrderNumBits)
[docs] def getSeed(self):
"""
Returns:
seed: Hash seed
"""
return self.getOrDefault(self.seed)
[docs] def getSumCollisions(self):
"""
Returns:
sumCollisions: Sums collisions if true, otherwise removes them
"""
return self.getOrDefault(self.sumCollisions)