def create_test_and_train_user_item(self):
'''
INPUT:
df_train - training dataframe
df_test - test dataframe
OUTPUT:
user_item_train - a user-item matrix of the training dataframe
(unique users for each row and unique articles for each column)
user_item_test - a user-item matrix of the testing dataframe
(unique users for each row and unique articles for each column)
'''
num_interactions = len(self.interactions_df)
len_train = int(70*num_interactions/100) # 70% of the df for train
len_test = num_interactions - len_train # 30% of the df for test
df_train = self.interactions_df.head(len_train)
df_test = self.interactions_df.tail(len_test)
# we reuse the create_user_item_matrix we defined earlier
user_item_train = Utils.create_user_item_matrix(df_train)
user_item_test = Utils.create_user_item_matrix(df_test)
return (user_item_train, user_item_test)
from classfile import RecommendationEngine
from utility import Utils
# import and cleandata sources
interactions_df = pd.read_csv('data/user-item-interactions.csv')
article_content_df = pd.read_csv('data/articles_community.csv')
del interactions_df['Unnamed: 0']
del article_content_df['Unnamed: 0']
email_encoded = Utils.email_mapper(interactions_df['email'])
del interactions_df['email']
interactions_df['user_id'] = email_encoded
# create a matrix of user-article interactions
user_item = Utils.create_user_item_matrix(interactions_df)
# create an instance of the Recommendation Engine that can be used for multiple situations
rec_engine = RecommendationEngine(interactions_df, article_content_df,
user_item)
# test the code for a few situations (expected returned output of 10 article titles)
# recommendations for an article
_id_type = 'article'
_id = 10
recommended_articles = rec_engine.make_recommendations(_id, _id_type)
print('Test article')
print(
'The following articles are recommended based on your query for {} id {}:'.
format(_id_type, _id))
print(recommended_articles)