# 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 VowpalWabbitFeaturizer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
inputCols (list): The names of the input columns (default: [Ljava.lang.String;@75da8bc6)
numBits (int): Number of bits used to mask (default: 30)
outputCol (str): The name of the output column (default: features)
prefixStringsWithColumnName (bool): Prefix string features with column name (default: true)
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 (default: 0)
seed (int): Hash seed (default: 0)
stringSplitInputCols (list): Input cols that should be split at word boundaries (default: [Ljava.lang.String;@f5992bb)
sumCollisions (bool): Sums collisions if true, otherwise removes them (default: true)
"""
@keyword_only
def __init__(self, inputCols=[], numBits=30, outputCol="features", prefixStringsWithColumnName=True, preserveOrderNumBits=0, seed=0, stringSplitInputCols=[], sumCollisions=True):
super(VowpalWabbitFeaturizer, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.vw.VowpalWabbitFeaturizer")
self.inputCols = Param(self, "inputCols", "inputCols: The names of the input columns (default: [Ljava.lang.String;@75da8bc6)")
self._setDefault(inputCols=[])
self.numBits = Param(self, "numBits", "numBits: Number of bits used to mask (default: 30)")
self._setDefault(numBits=30)
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column (default: features)")
self._setDefault(outputCol="features")
self.prefixStringsWithColumnName = Param(self, "prefixStringsWithColumnName", "prefixStringsWithColumnName: Prefix string features with column name (default: true)")
self._setDefault(prefixStringsWithColumnName=True)
self.preserveOrderNumBits = Param(self, "preserveOrderNumBits", "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 (default: 0)")
self._setDefault(preserveOrderNumBits=0)
self.seed = Param(self, "seed", "seed: Hash seed (default: 0)")
self._setDefault(seed=0)
self.stringSplitInputCols = Param(self, "stringSplitInputCols", "stringSplitInputCols: Input cols that should be split at word boundaries (default: [Ljava.lang.String;@f5992bb)")
self._setDefault(stringSplitInputCols=[])
self.sumCollisions = Param(self, "sumCollisions", "sumCollisions: Sums collisions if true, otherwise removes them (default: true)")
self._setDefault(sumCollisions=True)
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[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
Args:
inputCols (list): The names of the input columns (default: [Ljava.lang.String;@75da8bc6)
numBits (int): Number of bits used to mask (default: 30)
outputCol (str): The name of the output column (default: features)
prefixStringsWithColumnName (bool): Prefix string features with column name (default: true)
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 (default: 0)
seed (int): Hash seed (default: 0)
stringSplitInputCols (list): Input cols that should be split at word boundaries (default: [Ljava.lang.String;@f5992bb)
sumCollisions (bool): Sums collisions if true, otherwise removes them (default: true)
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def getNumBits(self):
"""
Returns:
int: Number of bits used to mask (default: 30)
"""
return self.getOrDefault(self.numBits)
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column (default: features)
"""
return self.getOrDefault(self.outputCol)
[docs] def getPrefixStringsWithColumnName(self):
"""
Returns:
bool: Prefix string features with column name (default: true)
"""
return self.getOrDefault(self.prefixStringsWithColumnName)
[docs] def getPreserveOrderNumBits(self):
"""
Returns:
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 (default: 0)
"""
return self.getOrDefault(self.preserveOrderNumBits)
[docs] def getSeed(self):
"""
Returns:
int: Hash seed (default: 0)
"""
return self.getOrDefault(self.seed)
[docs] def getSumCollisions(self):
"""
Returns:
bool: Sums collisions if true, otherwise removes them (default: true)
"""
return self.getOrDefault(self.sumCollisions)
[docs] def setNumBits(self, value):
"""
Args:
numBits: Number of bits used to mask (default: 30)
"""
self._set(numBits=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column (default: features)
"""
self._set(outputCol=value)
return self
[docs] def setPrefixStringsWithColumnName(self, value):
"""
Args:
prefixStringsWithColumnName: Prefix string features with column name (default: true)
"""
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 (default: 0)
"""
self._set(preserveOrderNumBits=value)
return self
[docs] def setSeed(self, value):
"""
Args:
seed: Hash seed (default: 0)
"""
self._set(seed=value)
return self
[docs] def setSumCollisions(self, value):
"""
Args:
sumCollisions: Sums collisions if true, otherwise removes them (default: true)
"""
self._set(sumCollisions=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.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)