synapse.ml.exploratory package
Submodules
synapse.ml.exploratory.AggregateBalanceMeasure module
- class synapse.ml.exploratory.AggregateBalanceMeasure.AggregateBalanceMeasure(java_obj=None, epsilon=1.0, errorTolerance=1e-12, outputCol='AggregateBalanceMeasure', sensitiveCols=None, verbose=False)[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
- epsilon = Param(parent='undefined', name='epsilon', doc='Epsilon value for Atkinson Index. Inverse of alpha (1 - alpha).')
- errorTolerance = Param(parent='undefined', name='errorTolerance', doc='Error tolerance value for Atkinson Index.')
- getEpsilon()[source]
- Returns:
Epsilon value for Atkinson Index. Inverse of alpha (1 - alpha).
- Return type:
epsilon
- getErrorTolerance()[source]
- Returns:
Error tolerance value for Atkinson Index.
- Return type:
errorTolerance
- getVerbose()[source]
- Returns:
Whether to show intermediate measures and calculations, such as Positive Rate.
- Return type:
verbose
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- sensitiveCols = Param(parent='undefined', name='sensitiveCols', doc='Sensitive columns to use.')
- setEpsilon(value)[source]
- Parameters:
epsilon¶ – Epsilon value for Atkinson Index. Inverse of alpha (1 - alpha).
- setErrorTolerance(value)[source]
- Parameters:
errorTolerance¶ – Error tolerance value for Atkinson Index.
- setParams(epsilon=1.0, errorTolerance=1e-12, outputCol='AggregateBalanceMeasure', sensitiveCols=None, verbose=False)[source]
Set the (keyword only) parameters
- setVerbose(value)[source]
- Parameters:
verbose¶ – Whether to show intermediate measures and calculations, such as Positive Rate.
- verbose = Param(parent='undefined', name='verbose', doc='Whether to show intermediate measures and calculations, such as Positive Rate.')
synapse.ml.exploratory.DistributionBalanceMeasure module
- class synapse.ml.exploratory.DistributionBalanceMeasure.DistributionBalanceMeasure(java_obj=None, featureNameCol='FeatureName', outputCol='DistributionBalanceMeasure', referenceDistribution=None, sensitiveCols=None, verbose=False)[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
- featureNameCol = Param(parent='undefined', name='featureNameCol', doc='Output column name for feature names.')
- getFeatureNameCol()[source]
- Returns:
Output column name for feature names.
- Return type:
featureNameCol
- getReferenceDistribution()[source]
- Returns:
An ordered list of reference distributions that correspond to each of the sensitive columns.
- Return type:
referenceDistribution
- getVerbose()[source]
- Returns:
Whether to show intermediate measures and calculations, such as Positive Rate.
- Return type:
verbose
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- referenceDistribution = Param(parent='undefined', name='referenceDistribution', doc='An ordered list of reference distributions that correspond to each of the sensitive columns.')
- sensitiveCols = Param(parent='undefined', name='sensitiveCols', doc='Sensitive columns to use.')
- setFeatureNameCol(value)[source]
- Parameters:
featureNameCol¶ – Output column name for feature names.
- setParams(featureNameCol='FeatureName', outputCol='DistributionBalanceMeasure', referenceDistribution=None, sensitiveCols=None, verbose=False)[source]
Set the (keyword only) parameters
- setReferenceDistribution(value)[source]
- Parameters:
referenceDistribution¶ – An ordered list of reference distributions that correspond to each of the sensitive columns.
- setVerbose(value)[source]
- Parameters:
verbose¶ – Whether to show intermediate measures and calculations, such as Positive Rate.
- verbose = Param(parent='undefined', name='verbose', doc='Whether to show intermediate measures and calculations, such as Positive Rate.')
synapse.ml.exploratory.FeatureBalanceMeasure module
- class synapse.ml.exploratory.FeatureBalanceMeasure.FeatureBalanceMeasure(java_obj=None, classACol='ClassA', classBCol='ClassB', featureNameCol='FeatureName', labelCol='label', outputCol='FeatureBalanceMeasure', sensitiveCols=None, verbose=False)[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
classACol¶ (str) – Output column name for the first feature value to compare.
classBCol¶ (str) – Output column name for the second feature value to compare.
featureNameCol¶ (str) – Output column name for feature names.
verbose¶ (bool) – Whether to show intermediate measures and calculations, such as Positive Rate.
- classACol = Param(parent='undefined', name='classACol', doc='Output column name for the first feature value to compare.')
- classBCol = Param(parent='undefined', name='classBCol', doc='Output column name for the second feature value to compare.')
- featureNameCol = Param(parent='undefined', name='featureNameCol', doc='Output column name for feature names.')
- getClassACol()[source]
- Returns:
Output column name for the first feature value to compare.
- Return type:
classACol
- getClassBCol()[source]
- Returns:
Output column name for the second feature value to compare.
- Return type:
classBCol
- getFeatureNameCol()[source]
- Returns:
Output column name for feature names.
- Return type:
featureNameCol
- getVerbose()[source]
- Returns:
Whether to show intermediate measures and calculations, such as Positive Rate.
- Return type:
verbose
- labelCol = Param(parent='undefined', name='labelCol', doc='label column name')
- outputCol = Param(parent='undefined', name='outputCol', doc='output column name')
- sensitiveCols = Param(parent='undefined', name='sensitiveCols', doc='Sensitive columns to use.')
- setClassACol(value)[source]
- Parameters:
classACol¶ – Output column name for the first feature value to compare.
- setClassBCol(value)[source]
- Parameters:
classBCol¶ – Output column name for the second feature value to compare.
- setFeatureNameCol(value)[source]
- Parameters:
featureNameCol¶ – Output column name for feature names.
- setParams(classACol='ClassA', classBCol='ClassB', featureNameCol='FeatureName', labelCol='label', outputCol='FeatureBalanceMeasure', sensitiveCols=None, verbose=False)[source]
Set the (keyword only) parameters
- setVerbose(value)[source]
- Parameters:
verbose¶ – Whether to show intermediate measures and calculations, such as Positive Rate.
- verbose = Param(parent='undefined', name='verbose', doc='Whether to show intermediate measures and calculations, such as Positive Rate.')
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+.