class Manager:
"""
Actual object to manage everything
"""
# reference to the entire data storage
__cache = None
# reference to object managing articles access and storage
__articles = None
# reference to the object doing the analysis
__analyzer = None
def __init__(self, config):
"""
Prepare the entire system's objects
config - the configuration object from the click library
"""
yaml_config = yaml.load(config.obj["config"])
config.obj["config"].close()
self.__cache = Cache(db_file=config.obj["database"])
self.__articles = Articles(key=yaml_config["api_key"], cache=self.__cache)
self.__analyzer = Analyzer()
def perform_search(self, phrase, training_size=1000):
"""
Perform the actual search either to cache or ny times
phrase - the phrase to search by
training_size - the amount of articles to use and fetch
returns the list of found articles
"""
return self.__articles.perform_search(phrase, training_size)
def analyze_results(self, article_list):
"""
Perform the regression analysis on the results and print
them out to the command line
"""
print("Using %i articles" % (len(article_list),))
self.__analyzer.process_data(article_list)
def predict_result(self, date):
"""
Make a prediction on a date
date - a datetime to make a prediction on
return a tuple of the type of article created from the analyzer
"""
return self.__analyzer.predict(date)
