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https://issues.apache.org/jira/browse/SPARK-24258?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16506399#comment-16506399
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Li Jin commented on SPARK-24258:
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I ran into [~mengxr] and chatted about this. Seems a good first step is to have
tensor type to be first-class type in Spark DataFrame. For operations, there is
concerns about having to add many many linear algebra functions in Spark
codebase, so it's not clear whether it's a good idea.
Any thoughts?
> SPIP: Improve PySpark support for ML Matrix and Vector types
> ------------------------------------------------------------
>
> Key: SPARK-24258
> URL: https://issues.apache.org/jira/browse/SPARK-24258
> Project: Spark
> Issue Type: New Feature
> Components: ML, PySpark
> Affects Versions: 2.3.0
> Reporter: Leif Walsh
> Priority: Major
>
> h1. Background and Motivation:
> In Spark ML ({{pyspark.ml.linalg}}), there are four column types you can
> construct, {{SparseVector}}, {{DenseVector}}, {{SparseMatrix}}, and
> {{DenseMatrix}}. In PySpark, you can construct one of these vectors with
> {{VectorAssembler}}, and then you can run python UDFs on these columns, and
> use {{toArray()}} to get numpy ndarrays and do things with them. They also
> have a small native API where you can compute {{dot()}}, {{norm()}}, and a
> few other things with them (I think these are computed in scala, not python,
> could be wrong).
> For statistical applications, having the ability to manipulate columns of
> matrix and vector values (from here on, I will use the term tensor to refer
> to arrays of arbitrary dimensionality, matrices are 2-tensors and vectors are
> 1-tensors) would be powerful. For example, you could use PySpark to reshape
> your data in parallel, assemble some matrices from your raw data, and then
> run some statistical computation on them using UDFs leveraging python
> libraries like statsmodels, numpy, tensorflow, and scikit-learn.
> I propose enriching the {{pyspark.ml.linalg}} types in the following ways:
> # Expand the set of column operations one can apply to tensor columns beyond
> the few functions currently available on these types. Ideally, the API
> should aim to be as wide as the numpy ndarray API, but would wrap Breeze
> operations. For example, we should provide {{DenseVector.outerProduct()}} so
> that a user could write something like {{df.withColumn("XtX",
> df["X"].outerProduct(df["X"]))}}.
> # Make sure all ser/de mechanisms (including Arrow) understand these types,
> and faithfully represent them as natural types in all languages (in scala and
> java, Breeze objects, in python, numpy ndarrays rather than the
> pyspark.ml.linalg types that wrap them, in SparkR, I'm not sure what, but
> something natural) when applying UDFs or collecting with {{toPandas()}}.
> # Improve the construction of these types from scalar columns. The
> {{VectorAssembler}} API is not very ergonomic. I propose something like
> {{df.withColumn("predictors", Vector.of(df["feature1"], df["feature2"],
> df["feature3"]))}}.
> h1. Target Personas:
> Data scientists, machine learning practitioners, machine learning library
> developers.
> h1. Goals:
> This would allow users to do more statistical computation in Spark natively,
> and would allow users to apply python statistical computation to data in
> Spark using UDFs.
> h1. Non-Goals:
> I suppose one non-goal is to reimplement something like statsmodels using
> Breeze data structures and computation. That could be seen as an effort to
> enrich Spark ML itself, but is out of scope of this effort. This effort is
> just to make it possible and easy to apply existing python libraries to
> tensor values in parallel.
> h1. Proposed API Changes:
> Add the above APIs to PySpark and the other language bindings. I think the
> list is:
> # {{pyspark.ml.linalg.Vector.of(*columns)}}
> # {{pyspark.ml.linalg.Matrix.of(<not sure what goes here, maybe we don't
> provide this>)}}
> # For each of the matrix and vector types in {{pyspark.ml.linalg}}, add more
> methods like {{outerProduct}}, {{matmul}}, {{kron}}, etc.
> https://docs.scipy.org/doc/numpy-1.14.0/reference/routines.linalg.html has a
> good list to look at.
> Also, change python UDFs so that these tensor types are passed to the python
> function not as \{Sparse,Dense\}\{Matrix,Vector\} objects that wrap
> {{numpy.ndarray}}, but as {{numpy.ndarray}} objects by themselves, and
> interpret return values that are {{numpy.ndarray}} objects back into the
> spark types.
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