cf.py

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

flag = False
re_flag = False


#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)
if flag:
    print(ratings.user_id.unique())
    print(ratings.user_id.unique().shape)
number_of_users = ratings.user_id.unique().shape[0]
number_of_items = ratings.movie_id.unique().shape[0]


#Reading items file:
i_cols = ['movie id', 'movie title' ,'release date','video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure', 'Animation', 'Children\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']
items = pd.read_csv('ml-100k/u.item', sep='|', names=i_cols, encoding='latin-1')

#Reading users file:
u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']
users = pd.read_csv('ml-100k/u.user', sep='|', names=u_cols) #,encoding='latin-1')


ratings_train = pd.read_csv('ml-100k/ua.base', sep='\t', names=r_cols)
ratings_test = pd.read_csv('ml-100k/ua.test', sep='\t', names=r_cols)
if flag:
    print(ratings_train.shape, ratings_test.shape)



# Forming main data matrix user item matrix
data_matrix = np.zeros((number_of_users, number_of_items))
for line in ratings.itertuples():
    # print(line)
    data_matrix[line[1] - 1, line[2] - 1] = line[
        3]  # first index in pandas dataframe is unique id, hence started from 1, storing the rating given by user to a particular item

if flag:
    print(data_matrix.shape)
# print(data_matrix)

user_similarity = 1-pairwise_distances(data_matrix, metric='correlation')
item_similarity = 1-pairwise_distances(data_matrix.T, metric='correlation')
if flag:
    print(user_similarity.shape)
# print(user_similarity)



title = pd.read_csv('ml-100k/u.item', sep='|', encoding='latin-1', header=None, usecols=[1])
titles = title[1]
ind = [i for i in range(0, len(titles))]
indices = pd.Series(ind, index=titles)
title_by_id = pd.Series(titles, index=ind)





def getRecommended(user):                                                         # using user user similarity
    recommended = set()
    tempRecommended = set()
    top_5_idx = np.argsort(user_similarity[user])[
                -6:].tolist()  # getting top 6 similar users, will remove top that is the element itself, and 5 will be returned
    print (top_5_idx)
    top_5_values = [user_similarity[user][i] for i in top_5_idx]
    print (top_5_values)
    top_5_idx.reverse()
    top_5_idx.pop(0)
    if flag:
        print(top_5_values)
# add already rated movies by the user too
    i = 0
    # user=1
    for val in data_matrix[user]:
        i += 1
        if (val != 0):
            tempRecommended.add(i)
            if flag:
                print(val)
    if re_flag:
        print("recommended originl----- ", tempRecommended)
    # tempRecommended=recommended.copy()
    count = 0
    rating5 = set()
    rating4 = set()
    rating3 = set()
    rating2 = set()
    rating1 = set()
    for idx in top_5_idx:
        i = -1
        temp = data_matrix[idx]
        for val in temp:
            i += 1
            if (val == 5):
                if flag:
                    print("here 5 ", i)
                if (i not in tempRecommended):
                    if flag:
                        print("hello")
                    count += 1
                    recommended.add(i)
                rating5.add(i)

            if (val == 4):
                rating4.add(i)

            if (val == 3):
                rating3.add(i)

            if (val == 2):
                rating2.add(i)

            if (val == 1):
                rating1.add(i)

            if (count >= 5):
                break
        if (count >= 5):
            break

    while (count < 5):
        j = -1
        for v in rating4:
            j += 1
            if (count >= 5):
                break
            if (j not in tempRecommended):
                recommended.add(j)
                count += 1
        j = -1
        for v in rating3:
            j += 1
            if (count >= 5):
                break
            if (j not in tempRecommended):
                recommended.add(j)
                count += 1
        j = -1
        for v in rating2:
            j += 1
            if (count >= 5):
                break
            if (j not in tempRecommended):
                recommended.add(j)
                count += 1
        j = -1
        for v in rating1:
            j += 1
            if (count >= 5):
                break
            if (j not in tempRecommended):
                recommended.add(j)
                count += 1
        if (count >= 5):
            break
    top_selected_movie_ids = recommended
    result = []
    for id in top_selected_movie_ids:
        # print(title_by_id[id])
        result.append(title_by_id[id])
    return recommended, result


#getRecommended(134)
if re_flag:
    print(" ****************************************************************************************************** ")


#print(recommended)




def getRecommendation(user):                                # using item item similarity
    recommended = set()
    tempRecommended = set()
    rating5 = set()
    rating4 = set()
    rating3 = set()
    rating2 = set()
    rating1 = set()
    i = -1
    count = 0
    temp = data_matrix[user]
    print(temp)
    for val in temp:
        i += 1
        if (val == 5):
            if flag:
                print("here 5 ", i)
                # if (i not in tempRecommended):
                # print("hello")
            count += 1
            recommended.add(i)
            rating5.add(i)

        if (val == 4):
            rating4.add(i)

        if (val == 3):
            rating3.add(i)

        if (val == 2):
            rating2.add(i)

        if (val == 1):
            rating1.add(i)

        if (count >= 5):
            break

    while (count < 5):
        j = -1
        for v in rating4:
            j += 1
            if (count >= 5):
                break

            recommended.add(j)
            count += 1
        j = -1
        for v in rating3:
            j += 1
            if (count >= 5):
                break

            recommended.add(j)
            count += 1
        j = -1
        for v in rating2:
            j += 1
            if (count >= 5):
                break

            recommended.add(j)
            count += 1
        j = -1
        for v in rating1:
            j += 1
            if (count >= 5):
                break

            recommended.add(j)
            count += 1

        if (count >= 5):
            break
    if flag:
        print(recommended)
    final_recommended = set()
    for item in recommended:
        top_5_idx = np.argsort(item_similarity[item])[
                    -6:].tolist()  # getting top 6 similar users, will remove top that is the element itself, and 5 will be returned
        if re_flag:
            print("------------------- ", top_5_idx)
        top_5_values = [item_similarity[item][i] for i in top_5_idx]
        if re_flag:
            print(top_5_values)
        top_5_idx.reverse()
        top_5_idx.pop(0)
        el = top_5_idx.pop(0)
        while (el in final_recommended and len(top_5_idx) != 0):
            el = top_5_idx.pop(0)
        final_recommended.add(el)
    if re_flag:
        print("........................................ ", final_recommended)
    top_selected_movie_ids = final_recommended
    result = []
    for id in top_selected_movie_ids:
        # print(title_by_id[id])
        result.append(title_by_id[id])
    return final_recommended, result


print("---------------------Collaborative Filtering---------------------------------------")
print()
print("User id :", 4)
print("-------------------Item to Item Similarity Based Recommendation------------------- ")

top_selected_movie_ids, rec_mov_titles = getRecommendation(4)
print("****************Movie Recommendations :************************")
for id in top_selected_movie_ids:
    print(title_by_id[id])
print("-----------------------------------------------------------------------------------")
print("-------------------User to User Similarity Based Recommendation-------------------")

top_selected_movie_ids, rec_mov_titles = getRecommended(4)
print("****************Movie Recommendations :************************")
for id in top_selected_movie_ids:
    print(title_by_id[id])


# print(top_5_idx)
# lst=data_matrix[top_5_idx[3]]
# for i in range (0,len(lst)):
# 	if (lst[i]!=0):
# 		print(lst[i])
#