refactored to separate out seed data generation

to allow changed
move filters function to pipeline
refactored + stats fixed

refactored to have a single function which trains a model on *both*
views (previously had separate and almost identical cochrane and pubmed
functions)

iteration 0 now represents a model which is trained *only* on the seed
data, and doesn’t use any distant supervision - gives a true baseline
reading of standard trained model
ignore pickles + CNLP.INI
moved LabeledAbstractReader to journalreaders.py

to avoid circular importing (which really didn’t work this time)
improved pipeline template processing

- previously used the dict[] method to retrieve functions, raised an
exception if features were not included for *every* word. this didn’t
work before for e.g. integer specific features
- amended so now does not automatically convert all features to strings
- useful for integer sizes or any other continous/ordered categorical
variables, previously treated all as unorded categories
removed N=xxx hack

regex not needed any more since we’re using a custom subclass of the
word_tokenizer which does this correctly now