[SPARK-4020] Do not rely on timeouts to remove failed block managers

If an executor fails without being scheduled to run any tasks, then `DAGScheduler` won't notify `BlockManagerMasterActor` that the associated block manager should be removed. Instead, the associated block manager will be expired only after a few rounds of heartbeat timeouts. In terms of removal treatment, there should really be no distinction between executors that have been scheduled tasks and those that have not.

The fix, then, is to add all known executors to `TaskSchedulerImpl`'s `activeExecutorIds` whether or not it has been scheduled a task. In fact, the existing comment above `activeExecutorIds` is
```
// Which executor IDs we have executors on
val activeExecutorIds = new HashSet[String]
```
not  "Which executors have been scheduled tasks thus far."

Author: Andrew Or <andrewor14@gmail.com>

Closes #2865 from andrewor14/active-executors and squashes the following commits:

ff3172b [Andrew Or] Add all known executors to `activeExecutorIds`
SPARK-3770: Make userFeatures accessible from python

https://issues.apache.org/jira/browse/SPARK-3770

We need access to the underlying latent user features from python. However, the userFeatures RDD from the MatrixFactorizationModel isn't accessible from the python bindings. I've added a method to the underlying scala class to turn the RDD[(Int, Array[Double])] to an RDD[String]. This is then accessed from the python recommendation.py

Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>

Closes #2636 from mdagost/mf_user_features and squashes the following commits:

c98f9e2 [Michelangelo D'Agostino] Added unit tests for userFeatures and productFeatures and merged master.
d5eadf8 [Michelangelo D'Agostino] Merge branch 'master' into mf_user_features
2481a2a [Michelangelo D'Agostino] Merged master and resolved conflict.
a6ffb96 [Michelangelo D'Agostino] Eliminated a function from our first approach to this problem that is no longer needed now that we added the fromTuple2RDD function.
2aa1bf8 [Michelangelo D'Agostino] Implemented a function called fromTuple2RDD in PythonMLLibAPI and used it to expose the MF userFeatures and productFeatures in python.
34cb2a2 [Michelangelo D'Agostino] A couple of lint cleanups and a comment.
cdd98e3 [Michelangelo D'Agostino] It's working now.
e1fbe5e [Michelangelo D'Agostino] Added scala function to stringify userFeatures for access in python.