def get_similar_problems(probID, SVDNeighbourhood=False): if SVDNeighbourhood: svd2 = SVDNeighbourhood() svd2.load_model(os.path.join(utils.get_add_dir(), 'ratings_neigh')) else: svd2 = SVD() svd2.load_model(os.path.join(utils.get_add_dir(), 'ratings')) return svd2.similar(probID)
class Collaborative_filtering(object):
def __init__(self, ratings_file,
movies): #No need to pass as ,will be provided in views.py
#self.users = users
self.movies = movies
self.K = 100
self.PERCENT_TRAIN = 85
#Need to provide a default file location for ratings.csv instead of loading everytime.run below 2lines only once
#or just provide this file instead.
#self.users.to_csv("/home/sourabhkondapaka/Desktop/ratingsss.csv",index= False)
self.ratings_file = ratings_file #Give your path to ratings.csv created from above 2 lines.
self.data = None
self.svd = None
self.recommend_movies_list = None
self.recommend_movies_ids = None
self.similar_movies_list = None
self.similar_movies_ids = None
self.movie_id = None
self.train = None
self.test = None
def compute_svd(self):
'''
ratings = pd.read_csv("/home/sourabhkondapaka/Desktop/ratingsss.csv",index_col= False)
ratings = ratings.ix[1:]
ratings.to_csv("/home/sourabhkondapaka/Desktop/ratingsss.csv",index = False)
self.data = Data()
self.data.load(self.ratings_file, sep=',', format={'col':0, 'row':1 ,'value':2, 'ids':float})
self.train , self.test = self.data.split_train_test(percent=self.PERCENT_TRAIN)
self.svd = SVD()
self.svd.set_data(self.train)
self.svd.compute(k=self.K, min_values=1, pre_normalize=None, mean_center=True, post_normalize=True)'''
self.data = Data()
self.data.load(self.ratings_file,
sep=',',
format={
'col': 0,
'row': 1,
'value': 2,
'ids': float
})
self.train, self.test = self.data.split_train_test(percent=85)
self.svd = SVDNeighbourhood()
self.svd.set_data(self.train)
self.svd.compute(k=100,
min_values=1,
pre_normalize=None,
mean_center=False,
post_normalize=True)
def similarity_measure(
self, movie1,
movie2): #gives a similarity measure value between -1 to 1
return round(self.svd.similarity(movie1, movie2), 4)
def recommend_movies(self, user_id):
l = self.svd.recommend(user_id, n=10, only_unknowns=True, is_row=False)
self.recommend_movies_list = []
self.recommend_movies_ids = []
for p in l:
#movie names
bb = str(movies.ix[movies['movie_id'] == p[0]]['title']).split()
q = bb.index('Name:')
bb = ' '.join(bb[1:q])
self.recommend_movies_list.append(bb)
#movie ids
gg = movies.ix[movies['movie_id'] == p[0]]
gg = gg.reset_index()
del gg['index']
gg = gg.ix[:, 0:2].as_matrix(columns=None).tolist()
self.recommend_movies_ids.append(gg[0][0])
return self.recommend_movies_list, self.recommend_movies_ids
def get_similar_movies(self,
movie1): #Returns a PYTHON list for similar movies.
movie1 = int(movie1)
l = self.svd.similar(movie1)
self.similar_movies_list = []
self.similar_movies_ids = []
l = l[1:]
for p in l:
#getting movie names
bb = str(movies.ix[movies['movie_id'] == p[0]]['title']).split()
q = bb.index('Name:')
bb = ' '.join(bb[1:q])
self.similar_movies_list.append(bb)
#getting movie id's
self.similar_movies_ids.append(p[0])
return self.similar_movies_list, self.similar_movies_ids
