CosineSimilarity_Genre_Vector.py

import pandas as pd
import numpy as np
from scipy import spatial

debug = False
flag = False
movies_cols = []
temp_genres = [i for i in range(5, 24)]
movies_cols += temp_genres

# print movies_cols
genre = pd.read_csv('ml-100k/u.item', sep='|', encoding='latin-1', header=None, usecols=movies_cols)

title = pd.read_csv('ml-100k/u.item', sep='|', encoding='latin-1', header=None, usecols=[1])
# Reading ratings file:
r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']
ratings = pd.read_csv('ml-100k/u.data', sep='\t', names=r_cols)


df = ratings[ratings.rating == 5]

df=df.groupby('user_id')['movie_id'].apply(list).reset_index(name='movie_ids')

g_shape = genre.shape
if debug:
    print(g_shape[0], g_shape[1])
genre_list = genre.apply(lambda x: x.tolist(), axis=1)
if debug:
    print(type(genre_list))
    print(genre_list.shape)
    print(genre_list.head())
    print(genre.as_matrix())

titles = title[1]
# genres = genre.as_matrix()
genres = genre.values
ind = [i for i in range(0, len(titles))]
indices = pd.Series(ind, index=titles)
title_by_id = pd.Series(titles, index=ind)
movies_by_user=pd.Series(df.movie_ids.values,index=df.user_id)
if debug:
    print movies_by_user
# movie_title = 'Toy Story (1995)'
user=1
if user in df.user_id.values:
    movie_ids_selected=movies_by_user[user]
else:
    movie_ids_selected = movies_by_user[1]
# print movie_ids_selected
ind_of_given_title = movie_ids_selected[0]
print ("Since the user has rated the movie '",title_by_id[ind_of_given_title],"' as 5 , so we recommend these movies.")
print("-------------------CONTENT BASED ALGORITHM---------------------")
print('User id: ' , user)
print("----------------------------------------------------------------")
# if debug:
#     print(ind_of_given_title)
genre_of_inp_title = genres[ind_of_given_title]
cosine_similarity_dict = {}
for i in range(0, len(titles)):
    cosine_sim = 1 - spatial.distance.cosine(genre_of_inp_title, genres[i])
    cosine_similarity_dict[i] = cosine_sim
if debug:
    print(cosine_similarity_dict)
sorted_cos_sim_movie_id_pairs = sorted([(v, k) for (k, v) in cosine_similarity_dict.items()], reverse=True)
top_selected = sorted_cos_sim_movie_id_pairs[:6]
if debug:
    print(top_selected)
top_selected_movie_ids = [top_selected[i][1] for i in range(len(top_selected))]
if debug:
    print(top_selected_movie_ids)
prstrg = "Movie Recommendations :".upper()
print(prstrg)
print()
result = []
for id in top_selected_movie_ids:
    if id != ind_of_given_title:
        print(title_by_id[id])
        result.append(title_by_id[id])