synapse.ml.featurize.text package

Submodules

synapse.ml.featurize.text.MultiNGram module

class synapse.ml.featurize.text.MultiNGram.MultiNGram(java_obj=None, inputCol=None, lengths=None, outputCol='MultiNGram_9617b7a20123_output')[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

inputCol (object) – The name of the input column
lengths (object) – the collection of lengths to use for ngram extraction
outputCol (object) – The name of the output column

getInputCol()[source]

Returns: The name of the input column
Return type: inputCol

static getJavaPackage()[source]: Returns package name String.

getLengths()[source]

Returns: the collection of lengths to use for ngram extraction
Return type: lengths

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

inputCol = Param(parent='undefined', name='inputCol', doc='The name of the input column')

lengths = Param(parent='undefined', name='lengths', doc='the collection of lengths to use for ngram extraction')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

classmethod read()[source]: Returns an MLReader instance for this class.

setInputCol(value)[source]

Parameters: inputCol – The name of the input column

setLengths(value)[source]

Parameters: lengths – the collection of lengths to use for ngram extraction

setOutputCol(value)[source]

Parameters: outputCol – The name of the output column

setParams(inputCol=None, lengths=None, outputCol='MultiNGram_9617b7a20123_output')[source]: Set the (keyword only) parameters

synapse.ml.featurize.text.PageSplitter module

class synapse.ml.featurize.text.PageSplitter.PageSplitter(java_obj=None, boundaryRegex='\\s', inputCol=None, maximumPageLength=5000, minimumPageLength=4500, outputCol='PageSplitter_f5af9447952f_output')[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

boundaryRegex (object) – how to split into words
inputCol (object) – The name of the input column
maximumPageLength (int) – the maximum number of characters to be in a page
minimumPageLength (int) – the the minimum number of characters to have on a page in order to preserve work boundaries
outputCol (object) – The name of the output column

boundaryRegex = Param(parent='undefined', name='boundaryRegex', doc='how to split into words')

getBoundaryRegex()[source]

Returns: how to split into words
Return type: boundaryRegex

getInputCol()[source]

Returns: The name of the input column
Return type: inputCol

static getJavaPackage()[source]: Returns package name String.

getMaximumPageLength()[source]

Returns: the maximum number of characters to be in a page
Return type: maximumPageLength

getMinimumPageLength()[source]

Returns: the the minimum number of characters to have on a page in order to preserve work boundaries
Return type: minimumPageLength

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

inputCol = Param(parent='undefined', name='inputCol', doc='The name of the input column')

maximumPageLength = Param(parent='undefined', name='maximumPageLength', doc='the maximum number of characters to be in a page')

minimumPageLength = Param(parent='undefined', name='minimumPageLength', doc='the the minimum number of characters to have on a page in order to preserve work boundaries')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

classmethod read()[source]: Returns an MLReader instance for this class.

setBoundaryRegex(value)[source]

Parameters: boundaryRegex – how to split into words

setInputCol(value)[source]

Parameters: inputCol – The name of the input column

setMaximumPageLength(value)[source]

Parameters: maximumPageLength – the maximum number of characters to be in a page

setMinimumPageLength(value)[source]

Parameters: minimumPageLength – the the minimum number of characters to have on a page in order to preserve work boundaries

setOutputCol(value)[source]

Parameters: outputCol – The name of the output column

setParams(boundaryRegex='\\s', inputCol=None, maximumPageLength=5000, minimumPageLength=4500, outputCol='PageSplitter_f5af9447952f_output')[source]: Set the (keyword only) parameters

synapse.ml.featurize.text.TextFeaturizer module

class synapse.ml.featurize.text.TextFeaturizer.TextFeaturizer(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)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator

Parameters

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(parent='undefined', name='binary', doc='If true, all nonegative word counts are set to 1')

caseSensitiveStopWords = Param(parent='undefined', name='caseSensitiveStopWords', doc=' Whether to do a case sensitive comparison over the stop words')

defaultStopWordLanguage = Param(parent='undefined', name='defaultStopWordLanguage', doc='Which language to use for the stop word remover, set this to custom to use the stopWords input')

getBinary()[source]

Returns: If true, all nonegative word counts are set to 1
Return type: binary

getCaseSensitiveStopWords()[source]

Returns: Whether to do a case sensitive comparison over the stop words
Return type: caseSensitiveStopWords

getDefaultStopWordLanguage()[source]

Returns: Which language to use for the stop word remover, set this to custom to use the stopWords input
Return type: defaultStopWordLanguage

getInputCol()[source]

Returns: The name of the input column
Return type: inputCol

static getJavaPackage()[source]: Returns package name String.

getMinDocFreq()[source]

Returns: The minimum number of documents in which a term should appear.
Return type: minDocFreq

getMinTokenLength()[source]

Returns: Minimum token length, >= 0.
Return type: minTokenLength

getNGramLength()[source]

Returns: The size of the Ngrams
Return type: nGramLength

getNumFeatures()[source]

Returns: Set the number of features to hash each document to
Return type: numFeatures

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getStopWords()[source]

Returns: The words to be filtered out.
Return type: stopWords

getToLowercase()[source]

Returns: Indicates whether to convert all characters to lowercase before tokenizing.
Return type: toLowercase

getTokenizerGaps()[source]

Returns: Indicates whether regex splits on gaps (true) or matches tokens (false).
Return type: tokenizerGaps

getTokenizerPattern()[source]

Returns: Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.
Return type: tokenizerPattern

getUseIDF()[source]

Returns: Whether to scale the Term Frequencies by IDF
Return type: useIDF

getUseNGram()[source]

Returns: Whether to enumerate N grams
Return type: useNGram

getUseStopWordsRemover()[source]

Returns: Whether to remove stop words from tokenized data
Return type: useStopWordsRemover

getUseTokenizer()[source]

Returns: Whether to tokenize the input
Return type: useTokenizer

inputCol = Param(parent='undefined', name='inputCol', doc='The name of the input column')

minDocFreq = Param(parent='undefined', name='minDocFreq', doc='The minimum number of documents in which a term should appear.')

minTokenLength = Param(parent='undefined', name='minTokenLength', doc='Minimum token length, >= 0.')

nGramLength = Param(parent='undefined', name='nGramLength', doc='The size of the Ngrams')

numFeatures = Param(parent='undefined', name='numFeatures', doc='Set the number of features to hash each document to')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

classmethod read()[source]: Returns an MLReader instance for this class.

setBinary(value)[source]

Parameters: binary – If true, all nonegative word counts are set to 1

setCaseSensitiveStopWords(value)[source]

Parameters: caseSensitiveStopWords – Whether to do a case sensitive comparison over the stop words

setDefaultStopWordLanguage(value)[source]

Parameters: defaultStopWordLanguage – Which language to use for the stop word remover, set this to custom to use the stopWords input

setInputCol(value)[source]

Parameters: inputCol – The name of the input column

setMinDocFreq(value)[source]

Parameters: minDocFreq – The minimum number of documents in which a term should appear.

setMinTokenLength(value)[source]

Parameters: minTokenLength – Minimum token length, >= 0.

setNGramLength(value)[source]

Parameters: nGramLength – The size of the Ngrams

setNumFeatures(value)[source]

Parameters: numFeatures – Set the number of features to hash each document to

setOutputCol(value)[source]

Parameters: outputCol – The name of the output column

setParams(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)[source]: Set the (keyword only) parameters

setStopWords(value)[source]

Parameters: stopWords – The words to be filtered out.

setToLowercase(value)[source]

Parameters: toLowercase – Indicates whether to convert all characters to lowercase before tokenizing.

setTokenizerGaps(value)[source]

Parameters: tokenizerGaps – Indicates whether regex splits on gaps (true) or matches tokens (false).

setTokenizerPattern(value)[source]

Parameters: tokenizerPattern – Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.

setUseIDF(value)[source]

Parameters: useIDF – Whether to scale the Term Frequencies by IDF

setUseNGram(value)[source]

Parameters: useNGram – Whether to enumerate N grams

setUseStopWordsRemover(value)[source]

Parameters: useStopWordsRemover – Whether to remove stop words from tokenized data

setUseTokenizer(value)[source]

Parameters: useTokenizer – Whether to tokenize the input

stopWords = Param(parent='undefined', name='stopWords', doc='The words to be filtered out.')

toLowercase = Param(parent='undefined', name='toLowercase', doc='Indicates whether to convert all characters to lowercase before tokenizing.')

tokenizerGaps = Param(parent='undefined', name='tokenizerGaps', doc='Indicates whether regex splits on gaps (true) or matches tokens (false).')

tokenizerPattern = Param(parent='undefined', name='tokenizerPattern', doc='Regex pattern used to match delimiters if gaps is true or tokens if gaps is false.')

useIDF = Param(parent='undefined', name='useIDF', doc='Whether to scale the Term Frequencies by IDF')

useNGram = Param(parent='undefined', name='useNGram', doc='Whether to enumerate N grams')

useStopWordsRemover = Param(parent='undefined', name='useStopWordsRemover', doc='Whether to remove stop words from tokenized data')

useTokenizer = Param(parent='undefined', name='useTokenizer', doc='Whether to tokenize the input')

Module contents

SynapseML is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.

SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. In this vein, SynapseML provides easy to use SparkML transformers for a wide variety of Microsoft Cognitive Services. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.

SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+.