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[SPARK-20214][ML] Make sure converted csc matrix has sorted indices
Liang-Chi Hsieh authored 
## What changes were proposed in this pull request?

`_convert_to_vector` converts a scipy sparse matrix to csc matrix for initializing `SparseVector`. However, it doesn't guarantee the converted csc matrix has sorted indices and so a failure happens when you do something like that:

    from scipy.sparse import lil_matrix
    lil = lil_matrix((4, 1))
    lil[1, 0] = 1
    lil[3, 0] = 2
    _convert_to_vector(lil.todok())

    File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 78, in _convert_to_vector
      return SparseVector(l.shape[0], csc.indices, csc.data)
    File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 556, in __init__
      % (self.indices[i], self.indices[i + 1]))
    TypeError: Indices 3 and 1 are not strictly increasing

A simple test can confirm that `dok_matrix.tocsc()` won't guarantee sorted indices:

    >>> from scipy.sparse import lil_matrix
    >>> lil = lil_matrix((4, 1))
    >>> lil[1, 0] = 1
    >>> lil[3, 0] = 2
    >>> dok = lil.todok()
    >>> csc = dok.tocsc()
    >>> csc.has_sorted_indices
    0
    >>> csc.indices
    array([3, 1], dtype=int32)

I checked the source codes of scipy. The only way to guarantee it is `csc_matrix.tocsr()` and `csr_matrix.tocsc()`.

## How was this patch tested?

Existing tests.

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #17532 from viirya/make-sure-sorted-indices.
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README.md

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to setup your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.4), but additional sub-packages have their own requirements (including numpy and pandas).