# 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.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
from pyspark.ml import PipelineModel
[docs]@inherit_doc
class TextFeaturizer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
binary (bool): If true, all nonegative word counts are set to 1
caseSensitiveStopWords (bool): Whether to do a case sensitive comparison over the stop words
defaultStopWordLanguage (object): Which language to use for the stop word remover, set this to custom to use the stopWords input
inputCol (object): The name of the input column
minDocFreq (int): The minimum number of documents in which a term should appear.
minTokenLength (int): Minimum token length, >= 0.
nGramLength (int): The size of the Ngrams
numFeatures (int): Set the number of features to hash each document to
outputCol (object): The name of the output column
stopWords (object): The words to be filtered out.
toLowercase (bool): Indicates whether to convert all characters to lowercase before tokenizing.
tokenizerGaps (bool): Indicates whether regex splits on gaps (true) or matches tokens (false).
tokenizerPattern (object): Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.
useIDF (bool): Whether to scale the Term Frequencies by IDF
useNGram (bool): Whether to enumerate N grams
useStopWordsRemover (bool): Whether to remove stop words from tokenized data
useTokenizer (bool): Whether to tokenize the input
"""
binary = Param(Params._dummy(), "binary", "If true, all nonegative word counts are set to 1", typeConverter=TypeConverters.toBoolean)
caseSensitiveStopWords = Param(Params._dummy(), "caseSensitiveStopWords", " Whether to do a case sensitive comparison over the stop words", typeConverter=TypeConverters.toBoolean)
defaultStopWordLanguage = Param(Params._dummy(), "defaultStopWordLanguage", "Which language to use for the stop word remover, set this to custom to use the stopWords input")
inputCol = Param(Params._dummy(), "inputCol", "The name of the input column")
minDocFreq = Param(Params._dummy(), "minDocFreq", "The minimum number of documents in which a term should appear.", typeConverter=TypeConverters.toInt)
minTokenLength = Param(Params._dummy(), "minTokenLength", "Minimum token length, >= 0.", typeConverter=TypeConverters.toInt)
nGramLength = Param(Params._dummy(), "nGramLength", "The size of the Ngrams", typeConverter=TypeConverters.toInt)
numFeatures = Param(Params._dummy(), "numFeatures", "Set the number of features to hash each document to", typeConverter=TypeConverters.toInt)
outputCol = Param(Params._dummy(), "outputCol", "The name of the output column")
stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out.")
toLowercase = Param(Params._dummy(), "toLowercase", "Indicates whether to convert all characters to lowercase before tokenizing.", typeConverter=TypeConverters.toBoolean)
tokenizerGaps = Param(Params._dummy(), "tokenizerGaps", "Indicates whether regex splits on gaps (true) or matches tokens (false).", typeConverter=TypeConverters.toBoolean)
tokenizerPattern = Param(Params._dummy(), "tokenizerPattern", "Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.")
useIDF = Param(Params._dummy(), "useIDF", "Whether to scale the Term Frequencies by IDF", typeConverter=TypeConverters.toBoolean)
useNGram = Param(Params._dummy(), "useNGram", "Whether to enumerate N grams", typeConverter=TypeConverters.toBoolean)
useStopWordsRemover = Param(Params._dummy(), "useStopWordsRemover", "Whether to remove stop words from tokenized data", typeConverter=TypeConverters.toBoolean)
useTokenizer = Param(Params._dummy(), "useTokenizer", "Whether to tokenize the input", typeConverter=TypeConverters.toBoolean)
@keyword_only
def __init__(
self,
java_obj=None,
binary=False,
caseSensitiveStopWords=False,
defaultStopWordLanguage="english",
inputCol=None,
minDocFreq=1,
minTokenLength=0,
nGramLength=2,
numFeatures=262144,
outputCol="TextFeaturizer_312f3a2bf3bf_output",
stopWords=None,
toLowercase=True,
tokenizerGaps=True,
tokenizerPattern="\\s+",
useIDF=True,
useNGram=False,
useStopWordsRemover=False,
useTokenizer=True
):
super(TextFeaturizer, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.featurize.text.TextFeaturizer", self.uid)
else:
self._java_obj = java_obj
self._setDefault(binary=False)
self._setDefault(caseSensitiveStopWords=False)
self._setDefault(defaultStopWordLanguage="english")
self._setDefault(minDocFreq=1)
self._setDefault(minTokenLength=0)
self._setDefault(nGramLength=2)
self._setDefault(numFeatures=262144)
self._setDefault(outputCol="TextFeaturizer_312f3a2bf3bf_output")
self._setDefault(toLowercase=True)
self._setDefault(tokenizerGaps=True)
self._setDefault(tokenizerPattern="\\s+")
self._setDefault(useIDF=True)
self._setDefault(useNGram=False)
self._setDefault(useStopWordsRemover=False)
self._setDefault(useTokenizer=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,
binary=False,
caseSensitiveStopWords=False,
defaultStopWordLanguage="english",
inputCol=None,
minDocFreq=1,
minTokenLength=0,
nGramLength=2,
numFeatures=262144,
outputCol="TextFeaturizer_312f3a2bf3bf_output",
stopWords=None,
toLowercase=True,
tokenizerGaps=True,
tokenizerPattern="\\s+",
useIDF=True,
useNGram=False,
useStopWordsRemover=False,
useTokenizer=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.featurize.text.TextFeaturizer"
@staticmethod
def _from_java(java_stage):
module_name=TextFeaturizer.__module__
module_name=module_name.rsplit(".", 1)[0] + ".TextFeaturizer"
return from_java(java_stage, module_name)
[docs] def setBinary(self, value):
"""
Args:
binary: If true, all nonegative word counts are set to 1
"""
self._set(binary=value)
return self
[docs] def setCaseSensitiveStopWords(self, value):
"""
Args:
caseSensitiveStopWords: Whether to do a case sensitive comparison over the stop words
"""
self._set(caseSensitiveStopWords=value)
return self
[docs] def setDefaultStopWordLanguage(self, value):
"""
Args:
defaultStopWordLanguage: Which language to use for the stop word remover, set this to custom to use the stopWords input
"""
self._set(defaultStopWordLanguage=value)
return self
[docs] def setInputCol(self, value):
"""
Args:
inputCol: The name of the input column
"""
self._set(inputCol=value)
return self
[docs] def setMinDocFreq(self, value):
"""
Args:
minDocFreq: The minimum number of documents in which a term should appear.
"""
self._set(minDocFreq=value)
return self
[docs] def setMinTokenLength(self, value):
"""
Args:
minTokenLength: Minimum token length, >= 0.
"""
self._set(minTokenLength=value)
return self
[docs] def setNGramLength(self, value):
"""
Args:
nGramLength: The size of the Ngrams
"""
self._set(nGramLength=value)
return self
[docs] def setNumFeatures(self, value):
"""
Args:
numFeatures: Set the number of features to hash each document to
"""
self._set(numFeatures=value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def setStopWords(self, value):
"""
Args:
stopWords: The words to be filtered out.
"""
self._set(stopWords=value)
return self
[docs] def setToLowercase(self, value):
"""
Args:
toLowercase: Indicates whether to convert all characters to lowercase before tokenizing.
"""
self._set(toLowercase=value)
return self
[docs] def setTokenizerGaps(self, value):
"""
Args:
tokenizerGaps: Indicates whether regex splits on gaps (true) or matches tokens (false).
"""
self._set(tokenizerGaps=value)
return self
[docs] def setTokenizerPattern(self, value):
"""
Args:
tokenizerPattern: Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.
"""
self._set(tokenizerPattern=value)
return self
[docs] def setUseIDF(self, value):
"""
Args:
useIDF: Whether to scale the Term Frequencies by IDF
"""
self._set(useIDF=value)
return self
[docs] def setUseNGram(self, value):
"""
Args:
useNGram: Whether to enumerate N grams
"""
self._set(useNGram=value)
return self
[docs] def setUseStopWordsRemover(self, value):
"""
Args:
useStopWordsRemover: Whether to remove stop words from tokenized data
"""
self._set(useStopWordsRemover=value)
return self
[docs] def setUseTokenizer(self, value):
"""
Args:
useTokenizer: Whether to tokenize the input
"""
self._set(useTokenizer=value)
return self
[docs] def getBinary(self):
"""
Returns:
binary: If true, all nonegative word counts are set to 1
"""
return self.getOrDefault(self.binary)
[docs] def getCaseSensitiveStopWords(self):
"""
Returns:
caseSensitiveStopWords: Whether to do a case sensitive comparison over the stop words
"""
return self.getOrDefault(self.caseSensitiveStopWords)
[docs] def getDefaultStopWordLanguage(self):
"""
Returns:
defaultStopWordLanguage: Which language to use for the stop word remover, set this to custom to use the stopWords input
"""
return self.getOrDefault(self.defaultStopWordLanguage)
[docs] def getInputCol(self):
"""
Returns:
inputCol: The name of the input column
"""
return self.getOrDefault(self.inputCol)
[docs] def getMinDocFreq(self):
"""
Returns:
minDocFreq: The minimum number of documents in which a term should appear.
"""
return self.getOrDefault(self.minDocFreq)
[docs] def getMinTokenLength(self):
"""
Returns:
minTokenLength: Minimum token length, >= 0.
"""
return self.getOrDefault(self.minTokenLength)
[docs] def getNGramLength(self):
"""
Returns:
nGramLength: The size of the Ngrams
"""
return self.getOrDefault(self.nGramLength)
[docs] def getNumFeatures(self):
"""
Returns:
numFeatures: Set the number of features to hash each document to
"""
return self.getOrDefault(self.numFeatures)
[docs] def getOutputCol(self):
"""
Returns:
outputCol: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getStopWords(self):
"""
Returns:
stopWords: The words to be filtered out.
"""
return self.getOrDefault(self.stopWords)
[docs] def getToLowercase(self):
"""
Returns:
toLowercase: Indicates whether to convert all characters to lowercase before tokenizing.
"""
return self.getOrDefault(self.toLowercase)
[docs] def getTokenizerGaps(self):
"""
Returns:
tokenizerGaps: Indicates whether regex splits on gaps (true) or matches tokens (false).
"""
return self.getOrDefault(self.tokenizerGaps)
[docs] def getTokenizerPattern(self):
"""
Returns:
tokenizerPattern: Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.
"""
return self.getOrDefault(self.tokenizerPattern)
[docs] def getUseIDF(self):
"""
Returns:
useIDF: Whether to scale the Term Frequencies by IDF
"""
return self.getOrDefault(self.useIDF)
[docs] def getUseNGram(self):
"""
Returns:
useNGram: Whether to enumerate N grams
"""
return self.getOrDefault(self.useNGram)
[docs] def getUseStopWordsRemover(self):
"""
Returns:
useStopWordsRemover: Whether to remove stop words from tokenized data
"""
return self.getOrDefault(self.useStopWordsRemover)
[docs] def getUseTokenizer(self):
"""
Returns:
useTokenizer: Whether to tokenize the input
"""
return self.getOrDefault(self.useTokenizer)
def _create_model(self, java_model):
try:
model = PipelineModel(java_obj=java_model)
model._transfer_params_from_java()
except TypeError:
model = PipelineModel._from_java(java_model)
return model
def _fit(self, dataset):
java_model = self._fit_java(dataset)
return self._create_model(java_model)