Add packages to requirements.txt - dependencies for scikit-learn

Numpy
Scipy
Scikit-learn
Import libraries for scikit-learn into score.py
Add notes and small modifications to hashtag extraction and json functions at the top of score.py

Json function will have to change to get data from database, rather than json files
Added parameters and updated docstring in extract_hashtags to reflect a separate list of labels that will be created at the same time that hashtags list is populated.
Added vectorize and classifier functions to score.py, to instantiate and train classifier. Untested at present.

Created functions and added docstrings.
Use CountVectorizer from sklearn to count occurrences within features list (hashtags)
Instantiate a Bernoulli Naive Bayes classifer.
Create cross-validator with Stratified Kfold method to split data in K parts.
Print precision and recall averages.
Return classifier.
Modified extract_hashtags function to label every hashtag, even those in the same tweet. In main(), init main HASHTAGS and LABELS lists.

Indented label_list.append within extract_hashtags to create a label for every hashtag.
Deleted code that referenced LIB_HASHTAGS and CONS_HASHTAGS, simplified to a single hashtags list with accompanying labels list.
ipython notebook files
Debugging in ipy notebook - WIP
Moved ipynb files into tests directory. Finished debugging classifier in ipy notebook. Updated score.py

TODO: make sure I understand metrics, recall, precision. I was getting an error with precision.append(p[1]) originally, maybe due to wrong indentation.

Add X as parameter to init_and_train_classifier.
Added note about parameter X to docstring of init_and_train_classifier
Slight modification to precision/recall to test my understanding (print statements)