synapse.ml.image package

Submodules

synapse.ml.image.SuperpixelTransformer module

class synapse.ml.image.SuperpixelTransformer.SuperpixelTransformer(java_obj=None, cellSize=16.0, inputCol=None, modifier=130.0, outputCol='SuperpixelTransformer_4c2c7fa0bb2c_output')[source]

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

Parameters

cellSize¶ (float) – Number that controls the size of the superpixels
inputCol¶ (str) – The name of the input column
modifier¶ (float) – Controls the trade-off spatial and color distance
outputCol¶ (str) – The name of the output column

cellSize = Param(parent='undefined', name='cellSize', doc='Number that controls the size of the superpixels')

getCellSize()[source]

Returns: Number that controls the size of the superpixels
Return type: cellSize

getInputCol()[source]

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

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

getModifier()[source]

Returns: Controls the trade-off spatial and color distance
Return type: modifier

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')

modifier = Param(parent='undefined', name='modifier', doc='Controls the trade-off spatial and color distance')

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

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

setCellSize(value)[source]

Parameters: cellSize¶ – Number that controls the size of the superpixels

setInputCol(value)[source]

Parameters: inputCol¶ – The name of the input column

setModifier(value)[source]

Parameters: modifier¶ – Controls the trade-off spatial and color distance

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(cellSize=16.0, inputCol=None, modifier=130.0, outputCol='SuperpixelTransformer_4c2c7fa0bb2c_output')[source]: Set the (keyword only) parameters

synapse.ml.image.UnrollBinaryImage module

class synapse.ml.image.UnrollBinaryImage.UnrollBinaryImage(java_obj=None, height=None, inputCol='image', nChannels=None, outputCol='UnrollImage_9d0090683a70_output', width=None)[source]

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

Parameters

height¶ (int) – the width of the image
inputCol¶ (str) – The name of the input column
nChannels¶ (int) – the number of channels of the target image
outputCol¶ (str) – The name of the output column
width¶ (int) – the width of the image

getHeight()[source]

Returns: the width of the image
Return type: height

getInputCol()[source]

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

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

getNChannels()[source]

Returns: the number of channels of the target image
Return type: nChannels

getOutputCol()[source]

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

getWidth()[source]

Returns: the width of the image
Return type: width

height = Param(parent='undefined', name='height', doc='the width of the image')

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

nChannels = Param(parent='undefined', name='nChannels', doc='the number of channels of the target image')

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

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

setHeight(value)[source]

Parameters: height¶ – the width of the image

setInputCol(value)[source]

Parameters: inputCol¶ – The name of the input column

setNChannels(value)[source]

Parameters: nChannels¶ – the number of channels of the target image

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(height=None, inputCol='image', nChannels=None, outputCol='UnrollImage_9d0090683a70_output', width=None)[source]: Set the (keyword only) parameters

setWidth(value)[source]

Parameters: width¶ – the width of the image

width = Param(parent='undefined', name='width', doc='the width of the image')

synapse.ml.image.UnrollImage module

class synapse.ml.image.UnrollImage.UnrollImage(java_obj=None, inputCol='image', outputCol='UnrollImage_a40f5105e353_output')[source]

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

Parameters

inputCol¶ (str) – The name of the input column
outputCol¶ (str) – 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.

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')

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

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(inputCol='image', outputCol='UnrollImage_a40f5105e353_output')[source]: Set the (keyword only) parameters

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+.