synapse.ml.explainers package
Submodules
synapse.ml.explainers.ICETransformer module
- class synapse.ml.explainers.ICETransformer.ICETransformer(java_obj=None, categoricalFeatures=[], dependenceNameCol='pdpBasedDependence', featureNameCol='featureNames', kind='individual', model=None, numSamples=None, numericFeatures=[], targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
_ICETransformer
synapse.ml.explainers.ImageLIME module
- class synapse.ml.explainers.ImageLIME.ImageLIME(java_obj=None, cellSize=16.0, inputCol=None, kernelWidth=0.75, metricsCol='r2', model=None, modifier=130.0, numSamples=900, outputCol='ImageLIME_d3760e2ec06e__output', regularization=0.0, samplingFraction=0.7, superpixelCol='superpixels', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
cellSize¶ (float) – Number that controls the size of the superpixels
kernelWidth¶ (float) – Kernel width. Default value: sqrt (number of features) * 0.75
modifier¶ (float) – Controls the trade-off spatial and color distance
regularization¶ (float) – Regularization param for the lasso. Default value: 0.
samplingFraction¶ (float) – The fraction of superpixels (for image) or tokens (for text) to keep on
superpixelCol¶ (str) – The column holding the superpixel decompositions
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- 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
- getKernelWidth()[source]
- Returns:
Kernel width. Default value: sqrt (number of features) * 0.75
- Return type:
kernelWidth
- getModifier()[source]
- Returns:
Controls the trade-off spatial and color distance
- Return type:
modifier
- getRegularization()[source]
- Returns:
Regularization param for the lasso. Default value: 0.
- Return type:
regularization
- getSamplingFraction()[source]
- Returns:
The fraction of superpixels (for image) or tokens (for text) to keep on
- Return type:
samplingFraction
- getSuperpixelCol()[source]
- Returns:
The column holding the superpixel decompositions
- Return type:
superpixelCol
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name')
- kernelWidth = Param(parent='undefined', name='kernelWidth', doc='Kernel width. Default value: sqrt (number of features) * 0.75')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- modifier = Param(parent='undefined', name='modifier', doc='Controls the trade-off spatial and color distance')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- regularization = Param(parent='undefined', name='regularization', doc='Regularization param for the lasso. Default value: 0.')
- samplingFraction = Param(parent='undefined', name='samplingFraction', doc='The fraction of superpixels (for image) or tokens (for text) to keep on')
- setCellSize(value)[source]
- Parameters:
cellSize¶ – Number that controls the size of the superpixels
- setKernelWidth(value)[source]
- Parameters:
kernelWidth¶ – Kernel width. Default value: sqrt (number of features) * 0.75
- setModifier(value)[source]
- Parameters:
modifier¶ – Controls the trade-off spatial and color distance
- setParams(cellSize=16.0, inputCol=None, kernelWidth=0.75, metricsCol='r2', model=None, modifier=130.0, numSamples=900, outputCol='ImageLIME_d3760e2ec06e__output', regularization=0.0, samplingFraction=0.7, superpixelCol='superpixels', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Set the (keyword only) parameters
- setRegularization(value)[source]
- Parameters:
regularization¶ – Regularization param for the lasso. Default value: 0.
- setSamplingFraction(value)[source]
- Parameters:
samplingFraction¶ – The fraction of superpixels (for image) or tokens (for text) to keep on
- setSuperpixelCol(value)[source]
- Parameters:
superpixelCol¶ – The column holding the superpixel decompositions
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- superpixelCol = Param(parent='undefined', name='superpixelCol', doc='The column holding the superpixel decompositions')
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
synapse.ml.explainers.ImageSHAP module
- class synapse.ml.explainers.ImageSHAP.ImageSHAP(java_obj=None, cellSize=16.0, infWeight=100000000.0, inputCol=None, metricsCol='r2', model=None, modifier=130.0, numSamples=None, outputCol='ImageSHAP_1addae04d339__output', superpixelCol='superpixels', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
cellSize¶ (float) – Number that controls the size of the superpixels
infWeight¶ (float) – The double value to represent infinite weight. Default: 1E8.
modifier¶ (float) – Controls the trade-off spatial and color distance
superpixelCol¶ (str) – The column holding the superpixel decompositions
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- 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
- getInfWeight()[source]
- Returns:
The double value to represent infinite weight. Default: 1E8.
- Return type:
infWeight
- getModifier()[source]
- Returns:
Controls the trade-off spatial and color distance
- Return type:
modifier
- getSuperpixelCol()[source]
- Returns:
The column holding the superpixel decompositions
- Return type:
superpixelCol
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- infWeight = Param(parent='undefined', name='infWeight', doc='The double value to represent infinite weight. Default: 1E8.')
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- modifier = Param(parent='undefined', name='modifier', doc='Controls the trade-off spatial and color distance')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- setCellSize(value)[source]
- Parameters:
cellSize¶ – Number that controls the size of the superpixels
- setInfWeight(value)[source]
- Parameters:
infWeight¶ – The double value to represent infinite weight. Default: 1E8.
- setModifier(value)[source]
- Parameters:
modifier¶ – Controls the trade-off spatial and color distance
- setParams(cellSize=16.0, infWeight=100000000.0, inputCol=None, metricsCol='r2', model=None, modifier=130.0, numSamples=None, outputCol='ImageSHAP_1addae04d339__output', superpixelCol='superpixels', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Set the (keyword only) parameters
- setSuperpixelCol(value)[source]
- Parameters:
superpixelCol¶ – The column holding the superpixel decompositions
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- superpixelCol = Param(parent='undefined', name='superpixelCol', doc='The column holding the superpixel decompositions')
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
synapse.ml.explainers.TabularLIME module
- class synapse.ml.explainers.TabularLIME.TabularLIME(java_obj=None, backgroundData=None, categoricalFeatures=[], inputCols=None, kernelWidth=0.75, metricsCol='r2', model=None, numSamples=1000, outputCol='TabularLIME_cef95e0deecc__output', regularization=0.0, targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
backgroundData¶ (object) – A dataframe containing background data
categoricalFeatures¶ (list) – Name of features that should be treated as categorical variables.
kernelWidth¶ (float) – Kernel width. Default value: sqrt (number of features) * 0.75
regularization¶ (float) – Regularization param for the lasso. Default value: 0.
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- backgroundData = Param(parent='undefined', name='backgroundData', doc='A dataframe containing background data')
- categoricalFeatures = Param(parent='undefined', name='categoricalFeatures', doc='Name of features that should be treated as categorical variables.')
- getBackgroundData()[source]
- Returns:
A dataframe containing background data
- Return type:
backgroundData
- getCategoricalFeatures()[source]
- Returns:
Name of features that should be treated as categorical variables.
- Return type:
categoricalFeatures
- getKernelWidth()[source]
- Returns:
Kernel width. Default value: sqrt (number of features) * 0.75
- Return type:
kernelWidth
- getRegularization()[source]
- Returns:
Regularization param for the lasso. Default value: 0.
- Return type:
regularization
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- inputCols = Param(parent='undefined', name='inputCols', doc='input column names')
- kernelWidth = Param(parent='undefined', name='kernelWidth', doc='Kernel width. Default value: sqrt (number of features) * 0.75')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- regularization = Param(parent='undefined', name='regularization', doc='Regularization param for the lasso. Default value: 0.')
- setBackgroundData(value)[source]
- Parameters:
backgroundData¶ – A dataframe containing background data
- setCategoricalFeatures(value)[source]
- Parameters:
categoricalFeatures¶ – Name of features that should be treated as categorical variables.
- setKernelWidth(value)[source]
- Parameters:
kernelWidth¶ – Kernel width. Default value: sqrt (number of features) * 0.75
- setParams(backgroundData=None, categoricalFeatures=[], inputCols=None, kernelWidth=0.75, metricsCol='r2', model=None, numSamples=1000, outputCol='TabularLIME_cef95e0deecc__output', regularization=0.0, targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Set the (keyword only) parameters
- setRegularization(value)[source]
- Parameters:
regularization¶ – Regularization param for the lasso. Default value: 0.
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
synapse.ml.explainers.TabularSHAP module
- class synapse.ml.explainers.TabularSHAP.TabularSHAP(java_obj=None, backgroundData=None, infWeight=100000000.0, inputCols=None, metricsCol='r2', model=None, numSamples=None, outputCol='TabularSHAP_d16e5c3c47f8__output', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
backgroundData¶ (object) – A dataframe containing background data
infWeight¶ (float) – The double value to represent infinite weight. Default: 1E8.
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- backgroundData = Param(parent='undefined', name='backgroundData', doc='A dataframe containing background data')
- getBackgroundData()[source]
- Returns:
A dataframe containing background data
- Return type:
backgroundData
- getInfWeight()[source]
- Returns:
The double value to represent infinite weight. Default: 1E8.
- Return type:
infWeight
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- infWeight = Param(parent='undefined', name='infWeight', doc='The double value to represent infinite weight. Default: 1E8.')
- inputCols = Param(parent='undefined', name='inputCols', doc='input column names')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- setBackgroundData(value)[source]
- Parameters:
backgroundData¶ – A dataframe containing background data
- setInfWeight(value)[source]
- Parameters:
infWeight¶ – The double value to represent infinite weight. Default: 1E8.
- setParams(backgroundData=None, infWeight=100000000.0, inputCols=None, metricsCol='r2', model=None, numSamples=None, outputCol='TabularSHAP_d16e5c3c47f8__output', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Set the (keyword only) parameters
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
synapse.ml.explainers.TextLIME module
- class synapse.ml.explainers.TextLIME.TextLIME(java_obj=None, inputCol=None, kernelWidth=0.75, metricsCol='r2', model=None, numSamples=1000, outputCol='TextLIME_4c9655cde17d__output', regularization=0.0, samplingFraction=0.7, targetClasses=[], targetClassesCol=None, targetCol='probability', tokensCol='tokens')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
kernelWidth¶ (float) – Kernel width. Default value: sqrt (number of features) * 0.75
regularization¶ (float) – Regularization param for the lasso. Default value: 0.
samplingFraction¶ (float) – The fraction of superpixels (for image) or tokens (for text) to keep on
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- getKernelWidth()[source]
- Returns:
Kernel width. Default value: sqrt (number of features) * 0.75
- Return type:
kernelWidth
- getRegularization()[source]
- Returns:
Regularization param for the lasso. Default value: 0.
- Return type:
regularization
- getSamplingFraction()[source]
- Returns:
The fraction of superpixels (for image) or tokens (for text) to keep on
- Return type:
samplingFraction
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name')
- kernelWidth = Param(parent='undefined', name='kernelWidth', doc='Kernel width. Default value: sqrt (number of features) * 0.75')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- regularization = Param(parent='undefined', name='regularization', doc='Regularization param for the lasso. Default value: 0.')
- samplingFraction = Param(parent='undefined', name='samplingFraction', doc='The fraction of superpixels (for image) or tokens (for text) to keep on')
- setKernelWidth(value)[source]
- Parameters:
kernelWidth¶ – Kernel width. Default value: sqrt (number of features) * 0.75
- setParams(inputCol=None, kernelWidth=0.75, metricsCol='r2', model=None, numSamples=1000, outputCol='TextLIME_4c9655cde17d__output', regularization=0.0, samplingFraction=0.7, targetClasses=[], targetClassesCol=None, targetCol='probability', tokensCol='tokens')[source]
Set the (keyword only) parameters
- setRegularization(value)[source]
- Parameters:
regularization¶ – Regularization param for the lasso. Default value: 0.
- setSamplingFraction(value)[source]
- Parameters:
samplingFraction¶ – The fraction of superpixels (for image) or tokens (for text) to keep on
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
- tokensCol = Param(parent='undefined', name='tokensCol', doc='The column holding the tokens')
synapse.ml.explainers.TextSHAP module
- class synapse.ml.explainers.TextSHAP.TextSHAP(java_obj=None, infWeight=100000000.0, inputCol=None, metricsCol='r2', model=None, numSamples=None, outputCol='TextSHAP_2c14c7a023f0__output', targetClasses=[], targetClassesCol=None, targetCol='probability', tokensCol='tokens')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
infWeight¶ (float) – The double value to represent infinite weight. Default: 1E8.
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- getInfWeight()[source]
- Returns:
The double value to represent infinite weight. Default: 1E8.
- Return type:
infWeight
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- infWeight = Param(parent='undefined', name='infWeight', doc='The double value to represent infinite weight. Default: 1E8.')
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- setInfWeight(value)[source]
- Parameters:
infWeight¶ – The double value to represent infinite weight. Default: 1E8.
- setParams(infWeight=100000000.0, inputCol=None, metricsCol='r2', model=None, numSamples=None, outputCol='TextSHAP_2c14c7a023f0__output', targetClasses=[], targetClassesCol=None, targetCol='probability', tokensCol='tokens')[source]
Set the (keyword only) parameters
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
- tokensCol = Param(parent='undefined', name='tokensCol', doc='The column holding the tokens')
synapse.ml.explainers.VectorLIME module
- class synapse.ml.explainers.VectorLIME.VectorLIME(java_obj=None, backgroundData=None, inputCol=None, kernelWidth=0.75, metricsCol='r2', model=None, numSamples=1000, outputCol='VectorLIME_2ad0edc838e2__output', regularization=0.0, targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
backgroundData¶ (object) – A dataframe containing background data
kernelWidth¶ (float) – Kernel width. Default value: sqrt (number of features) * 0.75
regularization¶ (float) – Regularization param for the lasso. Default value: 0.
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- backgroundData = Param(parent='undefined', name='backgroundData', doc='A dataframe containing background data')
- getBackgroundData()[source]
- Returns:
A dataframe containing background data
- Return type:
backgroundData
- getKernelWidth()[source]
- Returns:
Kernel width. Default value: sqrt (number of features) * 0.75
- Return type:
kernelWidth
- getRegularization()[source]
- Returns:
Regularization param for the lasso. Default value: 0.
- Return type:
regularization
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name')
- kernelWidth = Param(parent='undefined', name='kernelWidth', doc='Kernel width. Default value: sqrt (number of features) * 0.75')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- regularization = Param(parent='undefined', name='regularization', doc='Regularization param for the lasso. Default value: 0.')
- setBackgroundData(value)[source]
- Parameters:
backgroundData¶ – A dataframe containing background data
- setKernelWidth(value)[source]
- Parameters:
kernelWidth¶ – Kernel width. Default value: sqrt (number of features) * 0.75
- setParams(backgroundData=None, inputCol=None, kernelWidth=0.75, metricsCol='r2', model=None, numSamples=1000, outputCol='VectorLIME_2ad0edc838e2__output', regularization=0.0, targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Set the (keyword only) parameters
- setRegularization(value)[source]
- Parameters:
regularization¶ – Regularization param for the lasso. Default value: 0.
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
synapse.ml.explainers.VectorSHAP module
- class synapse.ml.explainers.VectorSHAP.VectorSHAP(java_obj=None, backgroundData=None, infWeight=100000000.0, inputCol=None, metricsCol='r2', model=None, numSamples=None, outputCol='VectorSHAP_a68d505cfda1__output', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
backgroundData¶ (object) – A dataframe containing background data
infWeight¶ (float) – The double value to represent infinite weight. Default: 1E8.
targetClasses¶ (list) – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
targetClassesCol¶ (str) – The name of the column that specifies the indices of the classes for multinomial classification models.
targetCol¶ (str) – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- backgroundData = Param(parent='undefined', name='backgroundData', doc='A dataframe containing background data')
- getBackgroundData()[source]
- Returns:
A dataframe containing background data
- Return type:
backgroundData
- getInfWeight()[source]
- Returns:
The double value to represent infinite weight. Default: 1E8.
- Return type:
infWeight
- getTargetClasses()[source]
- Returns:
The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- Return type:
targetClasses
- getTargetClassesCol()[source]
- Returns:
The name of the column that specifies the indices of the classes for multinomial classification models.
- Return type:
targetClassesCol
- getTargetCol()[source]
- Returns:
The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- Return type:
targetCol
- infWeight = Param(parent='undefined', name='infWeight', doc='The double value to represent infinite weight. Default: 1E8.')
- inputCol = Param(parent='undefined', name='inputCol', doc='input column name')
- metricsCol = Param(parent='undefined', name='metricsCol', doc='Column name for fitting metrics')
- model = Param(parent='undefined', name='model', doc='The model to be interpreted.')
- numSamples = Param(parent='undefined', name='numSamples', doc='Number of samples to generate.')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- setBackgroundData(value)[source]
- Parameters:
backgroundData¶ – A dataframe containing background data
- setInfWeight(value)[source]
- Parameters:
infWeight¶ – The double value to represent infinite weight. Default: 1E8.
- setParams(backgroundData=None, infWeight=100000000.0, inputCol=None, metricsCol='r2', model=None, numSamples=None, outputCol='VectorSHAP_a68d505cfda1__output', targetClasses=[], targetClassesCol=None, targetCol='probability')[source]
Set the (keyword only) parameters
- setTargetClasses(value)[source]
- Parameters:
targetClasses¶ – The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.
- setTargetClassesCol(value)[source]
- Parameters:
targetClassesCol¶ – The name of the column that specifies the indices of the classes for multinomial classification models.
- setTargetCol(value)[source]
- Parameters:
targetCol¶ – The column name of the prediction target to explain (i.e. the response variable). This is usually set to “prediction” for regression models and “probability” for probabilistic classification models. Default value: probability
- targetClasses = Param(parent='undefined', name='targetClasses', doc='The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.')
- targetClassesCol = Param(parent='undefined', name='targetClassesCol', doc='The name of the column that specifies the indices of the classes for multinomial classification models.')
- targetCol = Param(parent='undefined', name='targetCol', doc='The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability')
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+.