train_test_data.py

import pandas as pd

r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']
ratings_base = pd.read_csv('ml-100k/ua.base', sep='\t', names=r_cols, encoding='latin-1')
ratings_test = pd.read_csv('ml-100k/ua.test', sep='\t', names=r_cols, encoding='latin-1')
print ratings_base.shape, ratings_test.shape

#SFrame is an scalable, out-of-core dataframe, which allows you to work with datasets that are larger than the amount of RAM on your system.

import graphlab
train_data = graphlab.SFrame(ratings_base)
test_data = graphlab.SFrame(ratings_test)
#print train_data.head() #proper tabular format

'''
Simple popularity model
'''

popularity_model = graphlab.popularity_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating')
#print popularity_model

#Get recommendations for first 5 users and print them
#users = range(1,6) specifies user ID of first 5 users
#k=5 specifies top 5 recommendations to be given
popularity_recomm = popularity_model.recommend(users=range(1,6),k=5)
print 'Simple popularity model'
popularity_recomm.print_rows(num_rows=25)

ratings_base.groupby(by='movie_id')['rating'].mean().sort_values(ascending=False).head(20)

'''
Collaborative filtering
'''

#Train Model
item_sim_model = graphlab.item_similarity_recommender.create(train_data, user_id='user_id', item_id='movie_id', target='rating', similarity_type='pearson')

#Make Recommendations:
item_sim_recomm = item_sim_model.recommend(users=range(1,6),k=5)
item_sim_recomm.print_rows(num_rows=25)