mmlspark.image package¶

Submodules¶

mmlspark.image.ImageFeaturizer module¶

class mmlspark.image.ImageFeaturizer.ImageFeaturizer(cntkModel=None, cutOutputLayers=1, dropNa=True, inputCol=None, layerNames=None, outputCol=None)[source]¶

Bases: mmlspark.image._ImageFeaturizer._ImageFeaturizer

Parameters

SparkSession (SparkSession) – The SparkSession that will be used to find the model
ocation (str) – The location of the model, either on local or HDFS

setMiniBatchSize(size)[source]¶

setModel(modelSchema)[source]¶

setModelLocation(location)[source]¶

mmlspark.image.ImageSetAugmenter module¶

class mmlspark.image.ImageSetAugmenter.ImageSetAugmenter(flipLeftRight=True, flipUpDown=False, inputCol='image', outputCol=None)[source]¶

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

Parameters

flipLeftRight (bool) – Symmetric Left-Right (default: true)
flipUpDown (bool) – Symmetric Up-Down (default: false)
inputCol (str) – The name of the input column (default: image)
outputCol (str) – The name of the output column (default: [self.uid]_output)

getFlipLeftRight()[source]¶

Returns: Symmetric Left-Right (default: true)
Return type: bool

getFlipUpDown()[source]¶

Returns: Symmetric Up-Down (default: false)
Return type: bool

getInputCol()[source]¶

Returns: The name of the input column (default: image)
Return type: str

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

getOutputCol()[source]¶

Returns: The name of the output column (default: [self.uid]_output)
Return type: str

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

setFlipLeftRight(value)[source]¶

Parameters: flipLeftRight (bool) – Symmetric Left-Right (default: true)

setFlipUpDown(value)[source]¶

Parameters: flipUpDown (bool) – Symmetric Up-Down (default: false)

setInputCol(value)[source]¶

Parameters: inputCol (str) – The name of the input column (default: image)

setOutputCol(value)[source]¶

Parameters: outputCol (str) – The name of the output column (default: [self.uid]_output)

setParams(flipLeftRight=True, flipUpDown=False, inputCol='image', outputCol=None)[source]¶

Set the (keyword only) parameters

Parameters

flipLeftRight (bool) – Symmetric Left-Right (default: true)
flipUpDown (bool) – Symmetric Up-Down (default: false)
inputCol (str) – The name of the input column (default: image)
outputCol (str) – The name of the output column (default: [self.uid]_output)

mmlspark.image.UnrollBinaryImage module¶

class mmlspark.image.UnrollBinaryImage.UnrollBinaryImage(height=None, inputCol='image', nChannels=None, outputCol=None, width=None)[source]¶

Bases: mmlspark.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 (default: image)
nChannels (int) – the number of channels of the target image
outputCol (str) – The name of the output column (default: [self.uid]_output)
width (int) – the width of the image

getHeight()[source]¶

Returns: the width of the image
Return type: int

getInputCol()[source]¶

Returns: The name of the input column (default: image)
Return type: str

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

getNChannels()[source]¶

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

getOutputCol()[source]¶

Returns: The name of the output column (default: [self.uid]_output)
Return type: str

getWidth()[source]¶

Returns: the width of the image
Return type: int

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

setHeight(value)[source]¶

Parameters: height (int) – the width of the image

setInputCol(value)[source]¶

Parameters: inputCol (str) – The name of the input column (default: image)

setNChannels(value)[source]¶

Parameters: nChannels (int) – the number of channels of the target image

setOutputCol(value)[source]¶

Parameters: outputCol (str) – The name of the output column (default: [self.uid]_output)

setParams(height=None, inputCol='image', nChannels=None, outputCol=None, width=None)[source]¶

Set the (keyword only) parameters

Parameters

height (int) – the width of the image
inputCol (str) – The name of the input column (default: image)
nChannels (int) – the number of channels of the target image
outputCol (str) – The name of the output column (default: [self.uid]_output)
width (int) – the width of the image

setWidth(value)[source]¶

Parameters: width (int) – the width of the image

mmlspark.image.UnrollImage module¶

class mmlspark.image.UnrollImage.UnrollImage(inputCol='image', outputCol=None)[source]¶

Bases: mmlspark.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 (default: image)
outputCol (str) – The name of the output column (default: [self.uid]_output)

getInputCol()[source]¶

Returns: The name of the input column (default: image)
Return type: str

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

getOutputCol()[source]¶

Returns: The name of the output column (default: [self.uid]_output)
Return type: str

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

setInputCol(value)[source]¶

Parameters: inputCol (str) – The name of the input column (default: image)

setOutputCol(value)[source]¶

Parameters: outputCol (str) – The name of the output column (default: [self.uid]_output)

setParams(inputCol='image', outputCol=None)[source]¶

Set the (keyword only) parameters

Parameters

inputCol (str) – The name of the input column (default: image)
outputCol (str) – The name of the output column (default: [self.uid]_output)

Module contents¶

MicrosoftML is a library of Python classes to interface with the Microsoft scala APIs to utilize Apache Spark to create distibuted machine learning models.

MicrosoftML simplifies training and scoring classifiers and regressors, as well as facilitating the creation of models using the CNTK library, images, and text.