src/we_mean.py
def we_mean(tweets, test_tweets):
    """
    DESCRIPTION: 
            Given the calculated word embedings (of some dimension d) of the training and test set, 
            this function returns the tweet embeddings of the same d dimension by just averaging the
            vectors of each word in the same tweet. This is done for tweets and test_tweets pandas Dataframes.
    INPUT: 
            tweets: Dataframe of a set of tweets as a python strings
            test_tweets: Dataframe of a set of test tweets as a python strings
    OUTPUT: 
            we_tweets: tweet embeddings of d dimension
            we_test_tweets: test tweet embeddings of d dimension
    """
    words = get_embeddings_dictionary(tweets)
    print('\nBuilding tweets Embeddings')
    we_tweets = average_vectors(tweets, words)
    print('Building test tweets Embeddings')
    we_test_tweets = average_vectors(test_tweets, words)
    return we_tweets, we_test_tweets

    DESCRIPTION: 
            Given the calculated word embedings (of some dimension d) of the training and test set, 
            this function returns the tweet embeddings of the same d dimension by just averaging the
            vectors of each word in the same tweet. This is done for tweets and test_tweets pandas Dataframes.
    INPUT: 
            tweets: Dataframe of a set of tweets as a python strings
            test_tweets: Dataframe of a set of test tweets as a python strings
    OUTPUT: 
            we_tweets: tweet embeddings of d dimension
            we_test_tweets: test tweet embeddings of d dimension
    
Given the calculated word embedings (of some dimension d) of the training and test set,
this function returns the tweet embeddings of the same d dimension by just averaging the
vectors of each word in the same tweet. This is done for tweets and test_tweets pandas Dataframes.
Given the calculated word embedings ( of some dimension d ) of the training and test set this function returns the tweet embeddings of the same d dimension by just averaging the vectors of each word in the same tweet . This is done for tweets and test_tweets pandas Dataframes .
def we_mean(tweets, test_tweets):
    words = get_embeddings_dictionary(tweets)
    print('\nBuilding tweets Embeddings')
    we_tweets = average_vectors(tweets, words)
    print('Building test tweets Embeddings')
    we_test_tweets = average_vectors(test_tweets, words)
    return we_tweets, we_test_tweets
def we_mean ( tweets , test_tweets ) : words = get_embeddings_dictionary ( tweets ) print ( '\nBuilding tweets Embeddings' ) we_tweets = average_vectors ( tweets , words ) print ( 'Building test tweets Embeddings' ) we_test_tweets = average_vectors ( test_tweets , words ) return we_tweets , we_test_tweets
DESCRIPTION:
Given the calculated word embedings (of some dimension d) of the training and test set,
this function returns the tweet embeddings of the same d dimension by just averaging the
vectors of each word in the same tweet.
DESCRIPTION : Given the calculated word embedings ( of some dimension d ) of the training and test set this function returns the tweet embeddings of the same d dimension by just averaging the vectors of each word in the same tweet .
"""
    DESCRIPTION: 
            Given the calculated word embedings (of some dimension d) of the training and test set, 
            this function returns the tweet embeddings of the same d dimension by just averaging the
            vectors of each word in the same tweet. This is done for tweets and test_tweets pandas Dataframes.
    INPUT: 
            tweets: Dataframe of a set of tweets as a python strings
            test_tweets: Dataframe of a set of test tweets as a python strings
    OUTPUT: 
            we_tweets: tweet embeddings of d dimension
            we_test_tweets: test tweet embeddings of d dimension
    """
tweets
test_tweets