synapse.ml.vw package

Submodules

synapse.ml.vw.VectorZipper module

class synapse.ml.vw.VectorZipper.VectorZipper(java_obj=None, inputCols=None, outputCol=None)[source]

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

Parameters

inputCols (list) – The names of the input columns
outputCol (str) – The name of the output column

getInputCols()[source]

Returns: The names of the input columns
Return type: inputCols

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

getOutputCol()[source]

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

inputCols = Param(parent='undefined', name='inputCols', doc='The names of the input columns')

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

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

setInputCols(value)[source]

Parameters: inputCols – The names of the input columns

setOutputCol(value)[source]

Parameters: outputCol – The name of the output column

setParams(inputCols=None, outputCol=None)[source]: Set the (keyword only) parameters

synapse.ml.vw.VowpalWabbitClassificationModel module

class synapse.ml.vw.VowpalWabbitClassificationModel.VowpalWabbitClassificationModel(java_obj=None, additionalFeatures=None, featuresCol='features', labelCol='label', model=None, performanceStatistics=None, predictionCol='prediction', probabilityCol='probability', rawPredictionCol='rawPrediction', testArgs='', thresholds=None)[source]

Bases: synapse.ml.vw._VowpalWabbitClassificationModel._VowpalWabbitClassificationModel

getNativeModel()[source]: Get the binary native VW model.

getPerformanceStatistics()[source]

Returns: Performance statistics collected during training
Return type: performanceStatistics

getReadableModel()[source]

saveNativeModel(filename)[source]: Save the native model to a local or WASB remote location.

synapse.ml.vw.VowpalWabbitClassifier module

class synapse.ml.vw.VowpalWabbitClassifier.VowpalWabbitClassifier(java_obj=None, additionalFeatures=[], featuresCol='features', hashSeed=0, ignoreNamespaces=None, initialModel=None, interactions=None, l1=None, l2=None, labelCol='label', labelConversion=True, learningRate=None, numBits=18, numPasses=1, passThroughArgs='', powerT=None, predictionCol='prediction', probabilityCol='probability', rawPredictionCol='rawPrediction', thresholds=None, useBarrierExecutionMode=True, weightCol=None)[source]

Bases: synapse.ml.vw._VowpalWabbitClassifier._VowpalWabbitClassifier

setInitialModel(model)[source]: Initialize the estimator with a previously trained model.

synapse.ml.vw.VowpalWabbitContextualBandit module

class synapse.ml.vw.VowpalWabbitContextualBandit.VowpalWabbitContextualBandit(java_obj=None, additionalFeatures=[], additionalSharedFeatures=[], chosenActionCol='chosenAction', epsilon=0.05, featuresCol='features', hashSeed=0, ignoreNamespaces=None, initialModel=None, interactions=None, l1=None, l2=None, labelCol='label', learningRate=None, numBits=18, numPasses=1, parallelism=1, passThroughArgs='', powerT=None, predictionCol='prediction', probabilityCol='probability', sharedCol='shared', useBarrierExecutionMode=True, weightCol=None)[source]

Bases: synapse.ml.vw._VowpalWabbitContextualBandit._VowpalWabbitContextualBandit

parallelFit(dataset, param_maps)[source]

setInitialModel(model)[source]: Initialize the estimator with a previously trained model.

synapse.ml.vw.VowpalWabbitContextualBandit.to_java_params(sc, model, pyParamMap)[source]

synapse.ml.vw.VowpalWabbitContextualBanditModel module

class synapse.ml.vw.VowpalWabbitContextualBanditModel.VowpalWabbitContextualBanditModel(java_obj=None, additionalFeatures=[], additionalSharedFeatures=[], featuresCol='features', hashSeed=0, ignoreNamespaces=None, initialModel=None, interactions=None, l1=None, l2=None, labelCol='label', learningRate=None, model=None, numBits=18, numPasses=1, passThroughArgs='', performanceStatistics=None, powerT=None, predictionCol='prediction', rawPredictionCol='rawPrediction', sharedCol='shared', testArgs='', useBarrierExecutionMode=True, weightCol=None)[source]

Bases: synapse.ml.vw._VowpalWabbitContextualBanditModel._VowpalWabbitContextualBanditModel

getNativeModel()[source]: Get the binary native VW model.

getPerformanceStatistics()[source]

Returns: Performance statistics collected during training
Return type: performanceStatistics

getReadableModel()[source]

saveNativeModel(filename)[source]: Save the native model to a local or WASB remote location.

synapse.ml.vw.VowpalWabbitFeaturizer module

class synapse.ml.vw.VowpalWabbitFeaturizer.VowpalWabbitFeaturizer(java_obj=None, inputCols=[], numBits=30, outputCol='features', prefixStringsWithColumnName=True, preserveOrderNumBits=0, seed=0, stringSplitInputCols=[], sumCollisions=True)[source]

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

Parameters

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

getInputCols()[source]

Returns: The names of the input columns
Return type: inputCols

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

getNumBits()[source]

Returns: Number of bits used to mask
Return type: numBits

getOutputCol()[source]

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

getPrefixStringsWithColumnName()[source]

Returns: Prefix string features with column name
Return type: prefixStringsWithColumnName

getPreserveOrderNumBits()[source]

Returns: 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 type: preserveOrderNumBits

getSeed()[source]

Returns: Hash seed
Return type: seed

getStringSplitInputCols()[source]

Returns: Input cols that should be split at word boundaries
Return type: stringSplitInputCols

getSumCollisions()[source]

Returns: Sums collisions if true, otherwise removes them
Return type: sumCollisions

inputCols = Param(parent='undefined', name='inputCols', doc='The names of the input columns')

numBits = Param(parent='undefined', name='numBits', doc='Number of bits used to mask')

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

prefixStringsWithColumnName = Param(parent='undefined', name='prefixStringsWithColumnName', doc='Prefix string features with column name')

preserveOrderNumBits = Param(parent='undefined', name='preserveOrderNumBits', doc='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')

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

seed = Param(parent='undefined', name='seed', doc='Hash seed')

setInputCols(value)[source]

Parameters: inputCols – The names of the input columns

setNumBits(value)[source]

Parameters: numBits – Number of bits used to mask

setOutputCol(value)[source]

Parameters: outputCol – The name of the output column

setParams(inputCols=[], numBits=30, outputCol='features', prefixStringsWithColumnName=True, preserveOrderNumBits=0, seed=0, stringSplitInputCols=[], sumCollisions=True)[source]: Set the (keyword only) parameters

setPrefixStringsWithColumnName(value)[source]

Parameters: prefixStringsWithColumnName – Prefix string features with column name

setPreserveOrderNumBits(value)[source]

Parameters: 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

setSeed(value)[source]

Parameters: seed – Hash seed

setStringSplitInputCols(value)[source]

Parameters: stringSplitInputCols – Input cols that should be split at word boundaries

setSumCollisions(value)[source]

Parameters: sumCollisions – Sums collisions if true, otherwise removes them

stringSplitInputCols = Param(parent='undefined', name='stringSplitInputCols', doc='Input cols that should be split at word boundaries')

sumCollisions = Param(parent='undefined', name='sumCollisions', doc='Sums collisions if true, otherwise removes them')

synapse.ml.vw.VowpalWabbitInteractions module

class synapse.ml.vw.VowpalWabbitInteractions.VowpalWabbitInteractions(java_obj=None, inputCols=None, numBits=30, outputCol=None, sumCollisions=True)[source]

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

Parameters

inputCols (list) – The names of the input columns
numBits (int) – Number of bits used to mask
outputCol (str) – The name of the output column
sumCollisions (bool) – Sums collisions if true, otherwise removes them

getInputCols()[source]

Returns: The names of the input columns
Return type: inputCols

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

getNumBits()[source]

Returns: Number of bits used to mask
Return type: numBits

getOutputCol()[source]

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

getSumCollisions()[source]

Returns: Sums collisions if true, otherwise removes them
Return type: sumCollisions

inputCols = Param(parent='undefined', name='inputCols', doc='The names of the input columns')

numBits = Param(parent='undefined', name='numBits', doc='Number of bits used to mask')

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

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

setInputCols(value)[source]

Parameters: inputCols – The names of the input columns

setNumBits(value)[source]

Parameters: numBits – Number of bits used to mask

setOutputCol(value)[source]

Parameters: outputCol – The name of the output column

setParams(inputCols=None, numBits=30, outputCol=None, sumCollisions=True)[source]: Set the (keyword only) parameters

setSumCollisions(value)[source]

Parameters: sumCollisions – Sums collisions if true, otherwise removes them

sumCollisions = Param(parent='undefined', name='sumCollisions', doc='Sums collisions if true, otherwise removes them')

synapse.ml.vw.VowpalWabbitRegressionModel module

class synapse.ml.vw.VowpalWabbitRegressionModel.VowpalWabbitRegressionModel(java_obj=None, additionalFeatures=None, featuresCol='features', labelCol='label', model=None, performanceStatistics=None, predictionCol='prediction', rawPredictionCol='rawPrediction', testArgs='')[source]

Bases: synapse.ml.vw._VowpalWabbitRegressionModel._VowpalWabbitRegressionModel

getNativeModel()[source]: Get the binary native VW model.

getPerformanceStatistics()[source]

Returns: Performance statistics collected during training
Return type: performanceStatistics

getReadableModel()[source]

saveNativeModel(filename)[source]: Save the native model to a local or WASB remote location.

synapse.ml.vw.VowpalWabbitRegressor module

class synapse.ml.vw.VowpalWabbitRegressor.VowpalWabbitRegressor(java_obj=None, additionalFeatures=[], featuresCol='features', hashSeed=0, ignoreNamespaces=None, initialModel=None, interactions=None, l1=None, l2=None, labelCol='label', learningRate=None, numBits=18, numPasses=1, passThroughArgs='', powerT=None, predictionCol='prediction', useBarrierExecutionMode=True, weightCol=None)[source]

Bases: synapse.ml.vw._VowpalWabbitRegressor._VowpalWabbitRegressor

setInitialModel(model)[source]: Initialize the estimator with a previously trained model.

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+.