mmlspark.lime package¶

Submodules¶

mmlspark.lime.ImageLIME module¶

class mmlspark.lime.ImageLIME.ImageLIME(cellSize=16.0, inputCol=None, model=None, modifier=130.0, nSamples=900, outputCol=None, predictionCol='prediction', regularization=0.0, samplingFraction=0.3, superpixelCol='superpixels')[source]¶

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

Parameters

cellSize (double) – Number that controls the size of the superpixels (default: 16.0)
inputCol (str) – The name of the input column
model (object) – Model to try to locally approximate
modifier (double) – Controls the trade-off spatial and color distance (default: 130.0)
nSamples (int) – The number of samples to generate (default: 900)
outputCol (str) – The name of the output column
predictionCol (str) – prediction column name (default: prediction)
regularization (double) – regularization param for the lasso (default: 0.0)
samplingFraction (double) – The fraction of superpixels to keep on (default: 0.3)
superpixelCol (str) – The column holding the superpixel decompositions (default: superpixels)

getCellSize()[source]¶

Returns: Number that controls the size of the superpixels (default: 16.0)
Return type: double

getInputCol()[source]¶

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

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

getModel()[source]¶

Returns: Model to try to locally approximate
Return type: object

getModifier()[source]¶

Returns: Controls the trade-off spatial and color distance (default: 130.0)
Return type: double

getNSamples()[source]¶

Returns: The number of samples to generate (default: 900)
Return type: int

getOutputCol()[source]¶

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

getPredictionCol()[source]¶

Returns: prediction column name (default: prediction)
Return type: str

getRegularization()[source]¶

Returns: regularization param for the lasso (default: 0.0)
Return type: double

getSamplingFraction()[source]¶

Returns: The fraction of superpixels to keep on (default: 0.3)
Return type: double

getSuperpixelCol()[source]¶

Returns: The column holding the superpixel decompositions (default: superpixels)
Return type: str

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

setCellSize(value)[source]¶

Parameters: cellSize (double) – Number that controls the size of the superpixels (default: 16.0)

setInputCol(value)[source]¶

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

setModel(value)[source]¶

Parameters: model (object) – Model to try to locally approximate

setModifier(value)[source]¶

Parameters: modifier (double) – Controls the trade-off spatial and color distance (default: 130.0)

setNSamples(value)[source]¶

Parameters: nSamples (int) – The number of samples to generate (default: 900)

setOutputCol(value)[source]¶

Parameters: outputCol (str) – The name of the output column

setParams(cellSize=16.0, inputCol=None, model=None, modifier=130.0, nSamples=900, outputCol=None, predictionCol='prediction', regularization=0.0, samplingFraction=0.3, superpixelCol='superpixels')[source]¶

Set the (keyword only) parameters

Parameters

cellSize (double) – Number that controls the size of the superpixels (default: 16.0)
inputCol (str) – The name of the input column
model (object) – Model to try to locally approximate
modifier (double) – Controls the trade-off spatial and color distance (default: 130.0)
nSamples (int) – The number of samples to generate (default: 900)
outputCol (str) – The name of the output column
predictionCol (str) – prediction column name (default: prediction)
regularization (double) – regularization param for the lasso (default: 0.0)
samplingFraction (double) – The fraction of superpixels to keep on (default: 0.3)
superpixelCol (str) – The column holding the superpixel decompositions (default: superpixels)

setPredictionCol(value)[source]¶

Parameters: predictionCol (str) – prediction column name (default: prediction)

setRegularization(value)[source]¶

Parameters: regularization (double) – regularization param for the lasso (default: 0.0)

setSamplingFraction(value)[source]¶

Parameters: samplingFraction (double) – The fraction of superpixels to keep on (default: 0.3)

setSuperpixelCol(value)[source]¶

Parameters: superpixelCol (str) – The column holding the superpixel decompositions (default: superpixels)

mmlspark.lime.SuperpixelTransformer module¶

class mmlspark.lime.SuperpixelTransformer.SuperpixelTransformer(cellSize=16.0, inputCol=None, modifier=130.0, outputCol=None)[source]¶

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

Parameters

cellSize (double) – Number that controls the size of the superpixels (default: 16.0)
inputCol (str) – The name of the input column
modifier (double) – Controls the trade-off spatial and color distance (default: 130.0)
outputCol (str) – The name of the output column (default: [self.uid]_output)

getCellSize()[source]¶

Returns: Number that controls the size of the superpixels (default: 16.0)
Return type: double

getInputCol()[source]¶

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

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

getModifier()[source]¶

Returns: Controls the trade-off spatial and color distance (default: 130.0)
Return type: double

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.

setCellSize(value)[source]¶

Parameters: cellSize (double) – Number that controls the size of the superpixels (default: 16.0)

setInputCol(value)[source]¶

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

setModifier(value)[source]¶

Parameters: modifier (double) – Controls the trade-off spatial and color distance (default: 130.0)

setOutputCol(value)[source]¶

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

setParams(cellSize=16.0, inputCol=None, modifier=130.0, outputCol=None)[source]¶

Set the (keyword only) parameters

Parameters

cellSize (double) – Number that controls the size of the superpixels (default: 16.0)
inputCol (str) – The name of the input column
modifier (double) – Controls the trade-off spatial and color distance (default: 130.0)
outputCol (str) – The name of the output column (default: [self.uid]_output)

mmlspark.lime.TabularLIME module¶

class mmlspark.lime.TabularLIME.TabularLIME(inputCol=None, model=None, nSamples=1000, outputCol=None, predictionCol='prediction', regularization=0.0, samplingFraction=0.3)[source]¶

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

Parameters

inputCol (str) – The name of the input column
model (object) – Model to try to locally approximate
nSamples (int) – The number of samples to generate (default: 1000)
outputCol (str) – The name of the output column
predictionCol (str) – prediction column name (default: prediction)
regularization (double) – regularization param for the lasso (default: 0.0)
samplingFraction (double) – The fraction of superpixels to keep on (default: 0.3)

getInputCol()[source]¶

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

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

getModel()[source]¶

Returns: Model to try to locally approximate
Return type: object

getNSamples()[source]¶

Returns: The number of samples to generate (default: 1000)
Return type: int

getOutputCol()[source]¶

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

getPredictionCol()[source]¶

Returns: prediction column name (default: prediction)
Return type: str

getRegularization()[source]¶

Returns: regularization param for the lasso (default: 0.0)
Return type: double

getSamplingFraction()[source]¶

Returns: The fraction of superpixels to keep on (default: 0.3)
Return type: double

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

setInputCol(value)[source]¶

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

setModel(value)[source]¶

Parameters: model (object) – Model to try to locally approximate

setNSamples(value)[source]¶

Parameters: nSamples (int) – The number of samples to generate (default: 1000)

setOutputCol(value)[source]¶

Parameters: outputCol (str) – The name of the output column

setParams(inputCol=None, model=None, nSamples=1000, outputCol=None, predictionCol='prediction', regularization=0.0, samplingFraction=0.3)[source]¶

Set the (keyword only) parameters

Parameters

inputCol (str) – The name of the input column
model (object) – Model to try to locally approximate
nSamples (int) – The number of samples to generate (default: 1000)
outputCol (str) – The name of the output column
predictionCol (str) – prediction column name (default: prediction)
regularization (double) – regularization param for the lasso (default: 0.0)
samplingFraction (double) – The fraction of superpixels to keep on (default: 0.3)

setPredictionCol(value)[source]¶

Parameters: predictionCol (str) – prediction column name (default: prediction)

setRegularization(value)[source]¶

Parameters: regularization (double) – regularization param for the lasso (default: 0.0)

setSamplingFraction(value)[source]¶

Parameters: samplingFraction (double) – The fraction of superpixels to keep on (default: 0.3)

class mmlspark.lime.TabularLIME.TabularLIMEModel(java_model=None)[source]¶

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

Model fitted by TabularLIME.

This class is left empty on purpose. All necessary methods are exposed through inheritance.

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

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

mmlspark.lime.TabularLIMEModel module¶

class mmlspark.lime.TabularLIMEModel.TabularLIMEModel(columnMeans=None, columnSTDs=None, inputCol=None, model=None, nSamples=None, outputCol=None, predictionCol='prediction', regularization=None, samplingFraction=None)[source]¶

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

Parameters

columnMeans (list) – the means of each of the columns for perturbation
columnSTDs (list) – the standard deviations of each of the columns for perturbation
inputCol (str) – The name of the input column
model (object) – Model to try to locally approximate
nSamples (int) – The number of samples to generate
outputCol (str) – The name of the output column
predictionCol (str) – prediction column name (default: prediction)
regularization (double) – regularization param for the lasso
samplingFraction (double) – The fraction of superpixels to keep on

getColumnMeans()[source]¶

Returns: the means of each of the columns for perturbation
Return type: list

getColumnSTDs()[source]¶

Returns: the standard deviations of each of the columns for perturbation
Return type: list

getInputCol()[source]¶

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

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

getModel()[source]¶

Returns: Model to try to locally approximate
Return type: object

getNSamples()[source]¶

Returns: The number of samples to generate
Return type: int

getOutputCol()[source]¶

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

getPredictionCol()[source]¶

Returns: prediction column name (default: prediction)
Return type: str

getRegularization()[source]¶

Returns: regularization param for the lasso
Return type: double

getSamplingFraction()[source]¶

Returns: The fraction of superpixels to keep on
Return type: double

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

setColumnMeans(value)[source]¶

Parameters: columnMeans (list) – the means of each of the columns for perturbation

setColumnSTDs(value)[source]¶

Parameters: columnSTDs (list) – the standard deviations of each of the columns for perturbation

setInputCol(value)[source]¶

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

setModel(value)[source]¶

Parameters: model (object) – Model to try to locally approximate

setNSamples(value)[source]¶

Parameters: nSamples (int) – The number of samples to generate

setOutputCol(value)[source]¶

Parameters: outputCol (str) – The name of the output column

setParams(columnMeans=None, columnSTDs=None, inputCol=None, model=None, nSamples=None, outputCol=None, predictionCol='prediction', regularization=None, samplingFraction=None)[source]¶

Set the (keyword only) parameters

Parameters

columnMeans (list) – the means of each of the columns for perturbation
columnSTDs (list) – the standard deviations of each of the columns for perturbation
inputCol (str) – The name of the input column
model (object) – Model to try to locally approximate
nSamples (int) – The number of samples to generate
outputCol (str) – The name of the output column
predictionCol (str) – prediction column name (default: prediction)
regularization (double) – regularization param for the lasso
samplingFraction (double) – The fraction of superpixels to keep on

setPredictionCol(value)[source]¶

Parameters: predictionCol (str) – prediction column name (default: prediction)

setRegularization(value)[source]¶

Parameters: regularization (double) – regularization param for the lasso

setSamplingFraction(value)[source]¶

Parameters: samplingFraction (double) – The fraction of superpixels to keep on

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.