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hyukjinkwon 92cfbeeb5c [SPARK-21866][ML][PYTHON][FOLLOWUP] Few cleanups and fix image test failure in Python 3.6.0 / NumPy 1.13.3 
## What changes were proposed in this pull request?

Image test seems failed in Python 3.6.0 / NumPy 1.13.3. I manually tested as below:

```
======================================================================
ERROR: test_read_images (pyspark.ml.tests.ImageReaderTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/.../spark/python/pyspark/ml/tests.py", line 1831, in test_read_images
    self.assertEqual(ImageSchema.toImage(array, origin=first_row[0]), first_row)
  File "/.../spark/python/pyspark/ml/image.py", line 149, in toImage
    data = bytearray(array.astype(dtype=np.uint8).ravel())
TypeError: only integer scalar arrays can be converted to a scalar index

----------------------------------------------------------------------
Ran 1 test in 7.606s
```

To be clear, I think the error seems from NumPy - 75b2d5d427/numpy/core/src/multiarray/number.c (L947)

For a smaller scope:

```python
>>> import numpy as np
>>> bytearray(np.array([1]).astype(dtype=np.uint8))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: only integer scalar arrays can be converted to a scalar index
```

In Python 2.7 / NumPy 1.13.1, it prints:

```
bytearray(b'\x01')
```

So, here, I simply worked around it by converting it to bytes as below:

```python
>>> bytearray(np.array([1]).astype(dtype=np.uint8).tobytes())
bytearray(b'\x01')
```

Also, while looking into it again, I realised few arguments could be quite confusing, for example, `Row` that needs some specific attributes and `numpy.ndarray`. I added few type checking and added some tests accordingly. So, it shows an error message as below:

```
TypeError: array argument should be numpy.ndarray; however, it got [<class 'str'>].
```

## How was this patch tested?

Manually tested with `./python/run-tests`.

And also:

```
PYSPARK_PYTHON=python3 SPARK_TESTING=1 bin/pyspark pyspark.ml.tests ImageReaderTest
```

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #19835 from HyukjinKwon/SPARK-21866-followup.
2017-11-30 10:26:55 +09:00
..
docs [SPARK-21866][ML][PYSPARK] Adding spark image reader 2017-11-22 15:45:45 -08:00
lib [SPARK-21278][PYSPARK] Upgrade to Py4J 0.10.6 2017-07-05 16:33:23 -07:00
pyspark [SPARK-21866][ML][PYTHON][FOLLOWUP] Few cleanups and fix image test failure in Python 3.6.0 / NumPy 1.13.3 2017-11-30 10:26:55 +09:00
test_support [SPARK-19610][SQL] Support parsing multiline CSV files 2017-02-28 13:34:33 -08:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-18652][PYTHON] Include the example data and third-party licenses in pyspark package. 2016-12-07 06:09:27 +08:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [SPARK-21278][PYSPARK] Upgrade to Py4J 0.10.6 2017-07-05 16:33:23 -07:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests.py [SPARK-14280][BUILD][WIP] Update change-version.sh and pom.xml to add Scala 2.12 profiles and enable 2.12 compilation 2017-09-01 19:21:21 +01:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-22395][SQL][PYTHON] Fix the behavior of timestamp values for Pandas to respect session timezone 2017-11-28 16:45:22 +08:00

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 0.10.6), but additional sub-packages have their own requirements (including numpy and pandas).