[jira] [Updated] (SPARK-29212) Add common classes without using JVM backend

["Maciej Szymkiewicz (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29212?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Maciej Szymkiewicz updated SPARK-29212:
---------------------------------------
    Description: 
Copied from [https://github.com/apache/spark/pull/25776].

 
 Maciej's *Concern*:

*Use cases for public ML type hierarchy*
 * Add Python-only Transformer implementations:

 * 
 ** I am Python user and want to implement pure Python ML classifier without providing JVM backend.
 ** I want this classifier to be meaningfully positioned in the existing type hierarchy.
 ** However I have access only to high level classes ({{Estimator}}, {{Model}}, {{MLReader}} / {{MLReadable}}).
 * Run time parameter validation for both user defined (see above) and existing class hierarchy,

 * 
 ** I am a library developer who provides functions that are meaningful only for specific categories of {{Estimators}} - here classifiers.
 ** I want to validate that user passed argument is indeed a classifier:
 *** For built-in objects using "private" type hierarchy is not really satisfying (actually, what is the rationale behind making it "private"? If the goal is Scala API parity, and Scala counterparts are public, shouldn't these be too?).
 ** For user defined objects I can:
 *** Use duck typing (on {{setRawPredictionCol}} for classifier, on {{numClasses}} for classification model) but it hardly satisfying.
 *** Provide parallel non-abstract type hierarchy ({{Classifier}} or {{PythonClassifier}} and so on) and require users to implement such interfaces. That however would require separate logic for checking for built-in and and user-provided classes.
 *** Provide parallel abstract type hierarchy, register all existing built-in classes and require users to do the same.
 Clearly these are not satisfying solutions as they require either defensive programming or reinventing the same functionality for different 3rd party APIs.

 * Static type checking

 * 
 ** I am either end user or library developer and want to use PEP-484 annotations to indicate components that require classifier or classification model.

 * 
 ** Currently I can provide only imprecise annotations, [such as|https://github.com/zero323/pyspark-stubs/blob/dd5cfc9ef1737fc3ccc85c247c5116eaa4b9df18/third_party/3/pyspark/ml/classification.pyi#L241]
 def setClassifier(self, value: Estimator[M]) -> OneVsRest: ...
 or try to narrow things down using structural subtyping:
 class Classifier(Protocol, Estimator[M]): def setRawPredictionCol(self, value: str) -> Classifier: ... class Classifier(Protocol, Model): def setRawPredictionCol(self, value: str) -> Model: ... def numClasses(self) -> int: ...

(...)
 * First of all nothing in the original API indicated this. On the contrary, the original API clearly suggests that non-Java path is supported, by providing base classes (Params, Transformer, Estimator, Model, ML \{Reader,Writer}, ML\{Readable,Writable}) as well as Java specific implementations (JavaParams, JavaTransformer, JavaEstimator, JavaModel, JavaML\{Reader,Writer}, JavaML
{Readable,Writable}
).

 * Furthermore authoritative (IMHO) and open Python ML extensions exist (spark-sklearn is one of these, but if I recall correctly spark-deep-learning provides so pure-Python utilities). Personally I've seen quite a lot of private implementations, but that's just anecdotal evidence.

Let us assume for the sake of argument that above observations are irrelevant. I will argue that having complete, public type hierarchy is still desired:
 * Two out three use cases I described, can narrowed down to Java implementation only, though there are less compelling if we do that.
 * More importantly, public type hierarchy with Java specific extensions, is pyspark.ml standard. There is no reason to treat this specific case as an exception, especially when the implementations, is far from utilitarian (for example implementation-free JavaClassifierParams and JavaProbabilisticClassifierParams save, as for now, no practical purpose whatsoever).
  

Maciej's *Proposal*:
{code:java}
Python ML hierarchy should reflect Scala hierarchy first (@srowen), i.e.
class ClassifierParams: ...

class Predictor(Estimator,PredictorParams):
    def setLabelCol(self, value): ...
    def setFeaturesCol(self, value): ...
    def setPredictionCol(self, value): ...

class Classifier(Predictor, ClassifierParams):
    def setRawPredictionCol(self, value): ...

class PredictionModel(Model,PredictorParams):
    def setFeaturesCol(self, value): ...
    def setPredictionCol(self, value): ...
    def numFeatures(self): ...
    def predict(self, value): ...
and JVM interop should extend from this hierarchy, i.e.
class JavaPredictionModel(PredictionModel): ...
In other words it should be consistent with existing approach, where we have ABCs reflecting Scala API (Transformer, Estimator, Model) and so on, and Java* variants are their subclasses.
 {code}
 

*Formally*:

 * Python ML type hierarchy should reflect Scala counterpart, as long as it makes sense give differences between languages. This can reduce development burden, overhead of porting user application between both languages and overall consistency of the project.
 * Classes which are public in Scala should be "public" and implementation independent in Python. No {{Java}} prefix should be used.
 * Classes which are {{private}} or {{private[ml]}} in Scala, should be explicitly marked as internal, using PEP8 syntax. i.e
 {code:java}
 // code placeholder
 {code}
not
 {code:java}
 // code placeholder
 {code}
* Foo

 

 

 

  was:
copyed from [https://github.com/apache/spark/pull/25776.]

 

Maciej's *Concern*:

*Use cases for public ML type hierarchy*
 * Add Python-only Transformer implementations:

 ** I am Python user and want to implement pure Python ML classifier without providing JVM backend.
 ** I want this classifier to be meaningfully positioned in the existing type hierarchy.
 ** However I have access only to high level classes ({{Estimator}}, {{Model}}, {{MLReader}} / {{MLReadable}}).
 * Run time parameter validation for both user defined (see above) and existing class hierarchy,

 ** I am a library developer who provides functions that are meaningful only for specific categories of {{Estimators}} - here classifiers.
 ** I want to validate that user passed argument is indeed a classifier:
 *** For built-in objects using "private" type hierarchy is not really satisfying (actually, what is the rationale behind making it "private"? If the goal is Scala API parity, and Scala counterparts are public, shouldn't these be too?).
 ** For user defined objects I can:
 *** Use duck typing (on {{setRawPredictionCol}} for classifier, on {{numClasses}} for classification model) but it hardly satisfying.
 *** Provide parallel non-abstract type hierarchy ({{Classifier}} or {{PythonClassifier}} and so on) and require users to implement such interfaces. That however would require separate logic for checking for built-in and and user-provided classes.
 *** Provide parallel abstract type hierarchy, register all existing built-in classes and require users to do the same.
Clearly these are not satisfying solutions as they require either defensive programming or reinventing the same functionality for different 3rd party APIs.

 * Static type checking

 ** I am either end user or library developer and want to use PEP-484 annotations to indicate components that require classifier or classification model.

 ** Currently I can provide only imprecise annotations, [such as|https://github.com/zero323/pyspark-stubs/blob/dd5cfc9ef1737fc3ccc85c247c5116eaa4b9df18/third_party/3/pyspark/ml/classification.pyi#L241]
def setClassifier(self, value: Estimator[M]) -> OneVsRest: ...
or try to narrow things down using structural subtyping:
class Classifier(Protocol, Estimator[M]): def setRawPredictionCol(self, value: str) -> Classifier: ... class Classifier(Protocol, Model): def setRawPredictionCol(self, value: str) -> Model: ... def numClasses(self) -> int: ...

 

Maciej's *Proposal*:
{code:java}
Python ML hierarchy should reflect Scala hierarchy first (@srowen), i.e.
class ClassifierParams: ...

class Predictor(Estimator,PredictorParams):
    def setLabelCol(self, value): ...
    def setFeaturesCol(self, value): ...
    def setPredictionCol(self, value): ...

class Classifier(Predictor, ClassifierParams):
    def setRawPredictionCol(self, value): ...

class PredictionModel(Model,PredictorParams):
    def setFeaturesCol(self, value): ...
    def setPredictionCol(self, value): ...
    def numFeatures(self): ...
    def predict(self, value): ...
and JVM interop should extend from this hierarchy, i.e.
class JavaPredictionModel(PredictionModel): ...
In other words it should be consistent with existing approach, where we have ABCs reflecting Scala API (Transformer, Estimator, Model) and so on, and Java* variants are their subclasses.
 {code}
 

 

 

 

 


> Add common classes without using JVM backend
> --------------------------------------------
>
>                 Key: SPARK-29212
>                 URL: https://issues.apache.org/jira/browse/SPARK-29212
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML, PySpark
>    Affects Versions: 3.0.0
>            Reporter: zhengruifeng
>            Priority: Major
>
> Copied from [https://github.com/apache/spark/pull/25776].
>  
>  Maciej's *Concern*:
> *Use cases for public ML type hierarchy*
>  * Add Python-only Transformer implementations:
>  * 
>  ** I am Python user and want to implement pure Python ML classifier without providing JVM backend.
>  ** I want this classifier to be meaningfully positioned in the existing type hierarchy.
>  ** However I have access only to high level classes ({{Estimator}}, {{Model}}, {{MLReader}} / {{MLReadable}}).
>  * Run time parameter validation for both user defined (see above) and existing class hierarchy,
>  * 
>  ** I am a library developer who provides functions that are meaningful only for specific categories of {{Estimators}} - here classifiers.
>  ** I want to validate that user passed argument is indeed a classifier:
>  *** For built-in objects using "private" type hierarchy is not really satisfying (actually, what is the rationale behind making it "private"? If the goal is Scala API parity, and Scala counterparts are public, shouldn't these be too?).
>  ** For user defined objects I can:
>  *** Use duck typing (on {{setRawPredictionCol}} for classifier, on {{numClasses}} for classification model) but it hardly satisfying.
>  *** Provide parallel non-abstract type hierarchy ({{Classifier}} or {{PythonClassifier}} and so on) and require users to implement such interfaces. That however would require separate logic for checking for built-in and and user-provided classes.
>  *** Provide parallel abstract type hierarchy, register all existing built-in classes and require users to do the same.
>  Clearly these are not satisfying solutions as they require either defensive programming or reinventing the same functionality for different 3rd party APIs.
>  * Static type checking
>  * 
>  ** I am either end user or library developer and want to use PEP-484 annotations to indicate components that require classifier or classification model.
>  * 
>  ** Currently I can provide only imprecise annotations, [such as|https://github.com/zero323/pyspark-stubs/blob/dd5cfc9ef1737fc3ccc85c247c5116eaa4b9df18/third_party/3/pyspark/ml/classification.pyi#L241]
>  def setClassifier(self, value: Estimator[M]) -> OneVsRest: ...
>  or try to narrow things down using structural subtyping:
>  class Classifier(Protocol, Estimator[M]): def setRawPredictionCol(self, value: str) -> Classifier: ... class Classifier(Protocol, Model): def setRawPredictionCol(self, value: str) -> Model: ... def numClasses(self) -> int: ...
> (...)
>  * First of all nothing in the original API indicated this. On the contrary, the original API clearly suggests that non-Java path is supported, by providing base classes (Params, Transformer, Estimator, Model, ML \{Reader,Writer}, ML\{Readable,Writable}) as well as Java specific implementations (JavaParams, JavaTransformer, JavaEstimator, JavaModel, JavaML\{Reader,Writer}, JavaML
> {Readable,Writable}
> ).
>  * Furthermore authoritative (IMHO) and open Python ML extensions exist (spark-sklearn is one of these, but if I recall correctly spark-deep-learning provides so pure-Python utilities). Personally I've seen quite a lot of private implementations, but that's just anecdotal evidence.
> Let us assume for the sake of argument that above observations are irrelevant. I will argue that having complete, public type hierarchy is still desired:
>  * Two out three use cases I described, can narrowed down to Java implementation only, though there are less compelling if we do that.
>  * More importantly, public type hierarchy with Java specific extensions, is pyspark.ml standard. There is no reason to treat this specific case as an exception, especially when the implementations, is far from utilitarian (for example implementation-free JavaClassifierParams and JavaProbabilisticClassifierParams save, as for now, no practical purpose whatsoever).
>   
> Maciej's *Proposal*:
> {code:java}
> Python ML hierarchy should reflect Scala hierarchy first (@srowen), i.e.
> class ClassifierParams: ...
> class Predictor(Estimator,PredictorParams):
>     def setLabelCol(self, value): ...
>     def setFeaturesCol(self, value): ...
>     def setPredictionCol(self, value): ...
> class Classifier(Predictor, ClassifierParams):
>     def setRawPredictionCol(self, value): ...
> class PredictionModel(Model,PredictorParams):
>     def setFeaturesCol(self, value): ...
>     def setPredictionCol(self, value): ...
>     def numFeatures(self): ...
>     def predict(self, value): ...
> and JVM interop should extend from this hierarchy, i.e.
> class JavaPredictionModel(PredictionModel): ...
> In other words it should be consistent with existing approach, where we have ABCs reflecting Scala API (Transformer, Estimator, Model) and so on, and Java* variants are their subclasses.
>  {code}
>  
> *Formally*:
>  * Python ML type hierarchy should reflect Scala counterpart, as long as it makes sense give differences between languages. This can reduce development burden, overhead of porting user application between both languages and overall consistency of the project.
>  * Classes which are public in Scala should be "public" and implementation independent in Python. No {{Java}} prefix should be used.
>  * Classes which are {{private}} or {{private[ml]}} in Scala, should be explicitly marked as internal, using PEP8 syntax. i.e
>  {code:java}
>  // code placeholder
>  {code}
> not
>  {code:java}
>  // code placeholder
>  {code}
> * Foo
>  
>  
>  



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org