This is the python script made by my friend .How to integrate this file in my django project which contains all list of movies taken from the movierulz data set.Where should I integrate this code.
import numpy as np
import pandas as pd
# set some print options
np.set_printoptions(precision=4)
np.set_printoptions(threshold=5)
np.set_printoptions(suppress=True)
pd.set_option('precision', 3, 'notebook_repr_html', True, )
# init random gen
np.random.seed(2)
#users_file = "/media/sourabhkondapaka/Sourabh's/main_project/sandbox/ml-latest-small/ratings.csv"
#movies_file = "/media/sourabhkondapaka/Sourabh's/main_project/sandbox/ml-latest-small/movies.csv"
#users = pd.read_table(users_file,sep=',', header=None,names = ['user_id','movie_id','rating','timestamp'])
#movies = pd.read_table(movies_file, sep=',')
class popularity_based():
def __init__(self,users,movies):
self.users = users
self.movies = movies
self.user_id = None
self.mean_ratings = None
self.movielens= None
self.c = 0
def create(self):
self.movielens = pd.merge(users,movies)
self.movie_ratings = self.movielens.ix[:,1:3]
self.mean_ratings = self.movie_ratings.groupby('movie_id',as_index = True)['rating'].mean().sort_values(ascending = False)
self.mean_ratings = pd.DataFrame(self.mean_ratings).reset_index()
self.mean_ratings['title'] = self.mean_ratings['movie_id'].map(self.movies.set_index('movie_id')['title'])
def recommend(self, user_id,topu): #no arguement required here, just for the sake of uniformness across other recommender implementations
self.user_id = user_id
#From = self.c
#self.c += topu
#To = self.c
print(type(self.mean_ratings.as_matrix(columns=None)))
return self.mean_ratings.ix[:topu,'title'].as_matrix(columns = None)
