Add a new file in the feature_extractors folder containing a class inhering from Base, implementing the extract method:

from feature_extractor_base import Base


class SomeFeatureExtractor(Base):
    __cache__ = False

    def extract(self):
        return {'MOVIE_ID_1': {'my_crazy_feature': 1}}

Registration is automatic, just import your feature extractor in feature_extractors/__init__.py.

from keywords import SomeFeatureExtractor

You'll probably want to set __cache__ = False while you're developing. When __cache__ = True, the results of extract will be stored in a json file in the data/cache folder, which will prevent feature extractors that you aren't working on from running.

The feature extractor will have the instance variables self.session, self.models, and self.movie_ids set.

• self.session: A SQLAlchemy session; used for querying the db.
• self.models: SQLAlchemy models for each table in the db.
• self.movie_ids: An array of all of the movie ids we're using.

In addition, the feature extractor base class provides a few helper functions:

• segmented_movie_ids: this will yield movie_ids in chunks of 500 by default, making it a little easier to split up queries.
• movies_query: this is just a base query to get all the movies we care about, you can string addional stuff onto this.
• with_appearances: this can be used to get the number of times a feature appears in the dataset, and eliminate features that don't appear enough.

Checkout the KeywordFeatureExtractor for a decent example of what the logic should look like.

By default, FeatureExtractors are included in the baseline, and not exclusive to the oracle. To add a feature to the oracle, set oracle = True in the feature extractors class.

You'll want to install all the packages in requirements.txt. In addition, you should copy config.yaml.example to config.yaml, and change anything you need to for your mysql server.

Just run the feature_creator.py file.

python feature_creator.py --baseline

ID's for dev and test set will be in data folder, as will features.json.

To skip feature combinators, add --skip_combinators

Use the python logging module.

import logging
logging.debug("Some log message")

By default, it will log in feature_creator.log. Set --verbose to print logging messages to stdout.

Pretty solid docs starting at http://docs.sqlalchemy.org/en/rel_0_9/orm/tutorial.html.

The mysql char encoding has some issues, so text should be re-encoded before it's used. There's a helper method called encode in helper.py that can do this. Please use this method, so we can get rid of this (or at least work around it consistently).

Caveat: I wrote this quickly and there aren't tests. There are probably bugs. Some stuff could definitely be cleaned up, like session handling and imports.