Movie Recommender System

import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import pairwise_distances

df_rating = pd.read_json('movies_rating.json')
df_rating = df_rating[['userId','movieId','rating']]

df_mv = pd.read_csv('movies.csv')

print df_rating.shape

print df_mv.shape

new_index = range(len(df_rating))

df_rating = df_rating.reindex(new_index)

df_rating.dropna(inplace=True)

print df_rating.shape

df_rating.userId = df_rating.userId.astype(int)
df_rating.movieId = df_rating.movieId.astype(int)

print df_rating.head(10)

num_users = df_rating.userId.unique().shape[0]
num_movies = df_rating.movieId.unique().shape[0]

print '\nNumber of users = ' + str(num_users) + ' | Number of movies = ' + str(num_movies)

max_movie_id = df_rating.movieId.max()
min_movie_id = df_rating.movieId.min()

max_user_id = df_rating.userId.max()
min_user_id  = df_rating.userId.min()

print "Max MovieID : %d | Min MovieID : %d " %(max_movie_id,min_movie_id)
print "Max UserID : %d | Min UserID : %d " %(max_user_id,min_user_id)

#

df_movies.head()

len(df_mv.title.unique())

len(df_mv.movieId)

id_2_movie = {}

for movie_id in df_mv.movieId:
  index = df_mv.index[movie_id-1]
  if index > max_movie_id:
    break
  id_2_movie[movie_id] = df_mv.loc[index].title

cnt = 0
for line in df_rating.itertuples():
    print line
    cnt +=1
    if cnt > 20:
        break

df_matrix = np.zeros((num_users, max_movie_id))
for line in df_rating.itertuples():
    df_matrix[line[1]-1, line[2]-1] = line[3]

user_similarity = pairwise_distances(df_matrix, metric='cosine')

movie_similarity = pairwise_distances(df_matrix.T, metric='cosine')

user_similarity[:5]

movie_similarity[:5]

# Top 10 similar users for user id 200
print "users similar to user id 200: \n", pd.DataFrame(user_similarity).loc[199,pd.DataFrame(user_similarity).loc[199,:] > 0].sort_values(ascending=False)[0:10]

# Top 5 similar movie for movie id 9
print "movie similar to movie id 9: \n", pd.DataFrame(movie_similarity).loc[9,pd.DataFrame(movie_similarity).loc[9,:] > 0].sort_values(ascending=False)[0:5]

mv = pd.DataFrame(movie_similarity).loc[9,pd.DataFrame(movie_similarity).loc[9,:] > 0].sort_values(ascending=False)[0:5]

print "\t\t\t movie |  %s  | is similar to " % id_2_movie[31]
for i in mv.index:
  print id_2_movie[i]

# Function for item based rating prediction
def movie_based_prediction(rating_matrix, similarity_matrix):
    return rating_matrix.dot(similarity_matrix) / np.array([np.abs(similarity_matrix).sum(axis=1)])

# Function for user based rating prediction
def user_based_prediction(rating_matrix, similarity_matrix):
    mean_user_rating = rating_matrix.mean(axis=1)
    ratings_diff = (rating_matrix - mean_user_rating[:, np.newaxis])
    return mean_user_rating[:, np.newaxis] + similarity_matrix.dot(ratings_diff) / np.array([np.abs(similarity_matrix).sum(axis=1)]).T

movie_based_prediction = movie_based_prediction(df_matrix, movie_similarity)
user_based_prediction = user_based_prediction(df_matrix, user_similarity)

user_based = pd.DataFrame(user_based_prediction)
predictions = user_based.loc[5,pd.DataFrame(df_matrix).loc[5,:] == 0]
top = predictions.sort_values(ascending=False).head(n=5)
recommendations = pd.DataFrame(data=top)
recommendations.columns = ['Predicted Rating']
print "Prediction for Movies That User < #5 > has not rated yet! \n",recommendations

rec = [id_2_movie[i] for i in recommendations.index]

rec = pd.DataFrame(rec,columns=['Recommndation'])

print "Top Five Movies Recommendation for user < #5 > \n",rec

movie_based = pd.DataFrame(movie_based_prediction)
predictions = movie_based.loc[5,pd.DataFrame(df_matrix).loc[5,:] == 0]
top = predictions.sort_values(ascending=False).head(n=5)
recommendations = pd.DataFrame(data=top)
recommendations.columns = ['Predicted Rating']
print "Prediction for Movies That User < #5 > has not rated yet! \n",recommendations

rec = [id_2_movie[i] for i in recommendations.index]

rec = pd.DataFrame(rec,columns=['Recommndation'])

print "Top Five Movies Recommendation for user < #5 > \n",rec