def run_examples(): import random user = USER_DICT.keys()[random.randint(0, len(USER_DICT) - 1)] print user top = recommendations.top_matches(USER_DICT, user) print top recs = recommendations.get_recommendations(USER_DICT, user)[:10] print recs url = recs[0][1] more_top = recommendations.top_matches(recommendations.transform_prefs(USER_DICT), url) print more_top
def run_examples():
import random
user = USER_DICT.keys()[random.randint(0, len(USER_DICT) - 1)]
print user
top = recommendations.top_matches(USER_DICT, user)
print top
recs = recommendations.get_recommendations(USER_DICT, user)[:10]
print recs
url = recs[0][1]
more_top = recommendations.top_matches(
recommendations.transform_prefs(USER_DICT), url)
print more_top
import os
import pickle
import random
import recommendations as r
if __name__ == '__main__':
_, _, prefs = pickle.load(open('movielens.pkl'))
# To make item-based, transform prefs, so the format of prefs will be prefs[movie][user] = rating
prefs = r.transform_prefs(prefs)
# Split prefs into train and test prefs
movies = prefs.keys()
random.shuffle(movies)
movies_train, movies_test = movies[:int(0.9 * len(movies))], movies[int(0.1 * len(movies)):]
train = {m: prefs[m] for m in movies_train}
test = {m: prefs[m] for m in movies_test}
for movie in test:
sim_distances = []
sim_pearsons = []
for other_movie in train:
# Calculate distance using euclidean distance
sim_distances.append((r.sim_distance(prefs, movie, other_movie), other_movie))
# Calculate similarity using pearson
sim_pearsons.append((r.sim_pearson(prefs, movie, other_movie), other_movie))
# distance sort ascending
# Library imports
import recommendations, deliciousrec
# Initialize delicious users
delusers=deliciousrec.initialize_user_dict('programming')
# Add myself to the dataset
delusers['rogerfernandezg']={}
# Fills delicious users with data from delicious
deliciousrec.fill_items(delusers)
# Show recommendations for specific user
user=delusers.keys()[1]
print recommendations.top_matches(delusers,user)[0:10]
url=recommendations.get_recommendations(delusers,user)[0][1]
print recommendations.top_matches(recommendations.transform_prefs(delusers),url)
