Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/6992#discussion_r33420166
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -466,7 +557,8 @@ def predictOnValues(self, dstream):
def _test():
import doctest
- globs = globals().copy()
+ import pyspark.mllib.clustering
+ globs = pyspark.mllib.clustering.__dict__.copy()
--- End diff --
Yes, the original code will raise "Py4JError: Trying to call a package" at
https://github.com/apache/spark/pull/6992/files#diff-21c55b407050d37f67a2919470e047ebR324
.
I took a glance over related code and found in
https://github.com/apache/spark/blob/master/python/pyspark/mllib/recommendation.py#L209
it use __dict__copy(), so I try the same method here and it works well.
Then I investigate the difference between the two ways.
Running the following code:
```python
globs1 = globals().copy()
globs2 = pyspark.mllib.clustering.__dict__.copy()
print [k for k in glob1 if k not in glob2]
print [k for k in glob2 if k not in glob1]
```
and the output is:
```
['__loader__']
[]
```
It means that globs1(can't pass test) has one elements called
```__loader__``` more than globs2(can pass). I have no idea of what cause this.
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