Recommendation.py

#!/usr/bin/env python
# coding: utf-8

import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.neighbors import NearestNeighbors
import matplotlib.pyplot as plt
import seaborn as sns
movies = pd.read_csv("./db/movies.csv")
ratings = pd.read_csv("./db/ratings.csv")


movies.head()



ratings.head()


# using only ratings dataset and formatting it to our liking
final_dataset = ratings.pivot(index='movieId',columns='userId',values='rating')
final_dataset.head()



final_dataset.fillna(0,inplace=True)
final_dataset.head()



# list of movie ids and a count of users who rated it (index= movie id)
no_user_voted = ratings.groupby('movieId')['rating'].agg('count') 
# list of users and a count of the movies they rated (index =user id)
no_movies_voted = ratings.groupby('userId')['rating'].agg('count')
# print(no_user_voted)
# print(no_movies_voted)
f,ax = plt.subplots(1,1,figsize=(16,4))
ratings['rating'].plot(kind='hist')
plt.scatter(no_user_voted.index,no_user_voted,color='mediumseagreen')
plt.axhline(y=10,color='r')
plt.xlabel('MovieId')
plt.ylabel('No. of users voted')
plt.show()



# selecting movies where ratings are given by more than 10 users, : -> selecting all the userids for the selected row
final_dataset = final_dataset.loc[no_user_voted[no_user_voted > 10].index,:]

f,ax = plt.subplots(1,1,figsize=(16,4))
plt.scatter(no_movies_voted.index,no_movies_voted,color='mediumseagreen')
plt.axhline(y=50,color='r')
plt.xlabel('UserId')
plt.ylabel('No. of votes by user')
plt.show()

# : -> selecting all movieIds, and keeping only those users who have voted for more than 50 movies
final_dataset=final_dataset.loc[:,no_movies_voted[no_movies_voted > 50].index]





sample = np.array([[0,0,3,0,0],[4,0,0,0,2],[0,0,0,0,1]])
sparsity = 1.0 - ( np.count_nonzero(sample) / float(sample.size) )
print(sparsity)



csr_sample = csr_matrix(sample)
csr_data = csr_matrix(final_dataset.values)
final_dataset.reset_index(inplace=True)



knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)
knn.fit(csr_data)



def get_movie_recommendation(movie_name):
    n_movies_to_reccomend = 10
    movie_list = movies[movies['title'].str.contains(movie_name)]  
    if len(movie_list):        
        movie_idx= movie_list.iloc[0]['movieId']
        try:
            movie_idx = final_dataset[final_dataset['movieId'] == movie_idx].index[0]
            distances , indices = knn.kneighbors(csr_data[movie_idx],n_neighbors=n_movies_to_reccomend+1)    
            rec_movie_indices = sorted(list(zip(indices.squeeze().tolist(),distances.squeeze().tolist())),key=lambda x: x[1])[:0:-1]
            recommend_frame = []
            for val in rec_movie_indices:
                movie_idx = final_dataset.iloc[val[0]]['movieId']
                idx = movies[movies['movieId'] == movie_idx].index
                recommend_frame.append({'Title':movies.iloc[idx]['title'].values[0],'Distance':val[1]})
            df = pd.DataFrame(recommend_frame,index=range(1,n_movies_to_reccomend+1))
            return df
        except:
            return "No movies to recommend"
    else:
        return "No movies found. Please check your input"


get_movie_recommendation('Fifty Shades of Grey')