PPRM-OSN/code/PPRM.py

import json
#from pprint import pprint
import csv
from collections import Counter
from sklearn.metrics.pairwise import cosine_similarity
from mlxtend.frequent_patterns import apriori
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
from scipy import sparse
import numpy as np

######################DATASET INFORMATION##########################################
# The data was collected from the music streaming service Deezer (November 2017).
# These datasets represent friendship networks of users from 3 European countries.
# Nodes represent the users and edges are the mutual friendships. We reindexed the
# nodes in order to achieve a certain level of anonimity. The csv files contain the
# edges -- nodes are indexed from 0. The json files contain the genre preferences of
# users -- each key is a user id, the genres loved are given as lists. Genre notations
# are consistent across users.In each dataset users could like 84 distinct genres.
# Liked genre lists were compiled based on the liked song lists. The countries included
# are Romania, Croatia and Hungary. For each dataset we listed the number of nodes an edges.


with open('RO_genres.json') as data_file:
    data = json.load(data_file)

'#print(data.keys())'

users = []                              # Users in the network who uses the service
items = []                              # Items liked by users in the network
recommendations = []                    # Recommendations generated to the users after mining frequent itemsets

for key in data.keys():                 # Retreiving the ID of each user
    users.append(key)

for val in data.values():               # Retrieving the ITEMS liked by each user in the network
    items.append(val)

'#print(users)'
'#Users in the network, for example:'
#['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18',...,'41772']

'#print(items)'
'#Items liked by all te users in the network, for example:'
#['Dance', 'Soul & Funk', 'Pop', 'Musicals', 'Contemporary R&B', 'Indie Pop', 'Alternative'],

res = items
my_df = pd.DataFrame(res)
my_df.to_csv('out.csv', index=False, header=False)
'#print(my_df.head())'

'# Transposing the items and users into Binary matrix'

te = TransactionEncoder()
te_ary = te.fit(items).transform(items)
df = pd.DataFrame(te_ary, columns=te.columns_)
'#print(df.head())'

'# prints the Binary matrix elements, for example:'
#    Acoustic Blues  African Music     ...      Vocal jazz  West Coast
# 0           False          False     ...           False       False
# 1           False          False     ...           False       False
# 2           False          False     ...           False       False
# 3           False          False     ...           False       False
# 4           False          False     ...           False       False
'#print(te.columns_)'

# Resulting binary matrix to csv file

res = df
my_df = pd.DataFrame(res)
my_df.to_csv('result.csv', index=True, header=True)


data = pd.read_csv('result.csv')
data.rename(columns={'Unnamed: 0': 'user'}, inplace=True)
'#print(data.head())'

'# prints the Binary matrix elements in result.csv, for example:'
# user  Acoustic Blues        ...      Vocal jazz  West Coast
# 0     0           False     ...           False       False
# 1     1           False     ...           False       False
# 2     2           False     ...           False       False
# 3     3           False     ...           False       False
# 4     4           False     ...           False       False


data_items = data.drop('user', 1)

print('Dimension of loaded data is:', np.ndim(data_items))

interest_group_centroids = []                               # cluster centriods on which the interest groups are formed
interest_groups = []                                        # Most similar items for each centroid in the interest group
items_len = len(data_items.columns)                         # lengh of the items in the dataset
length = []                                                 # stores the index of the centroids

print('\n\n#########################################CENTROIDS#####################################################\n\n')

p = (items_len-1) // 5
r = p
length.append(p)

for index in range(0, 4):
    items_len = int(round(r + p))
    r = items_len
    length.append(items_len)
'#print(length)'
'#Index of the centroid elements, for example:'
#[16, 32, 48, 64, 80]

'# Calculating the centroids based on the length of the items in the DATASET: result.csv'

for index in length:                                        # for each centroid in the length
    centroids = data_items.columns.values[index]
    interest_group_centroids.append(centroids)
#print('The Centroids are = ', interest_group_centroids, '\n\n')
#For example: The Centroids are =  ['Comedy', 'Electro Hip Hop', 'Jazz Hip Hop', 'Rap/Hip Hop', 'Tropical']

print('\n\n#########################################ITEM-ITEM_SIMILARITY##########################################\n\n')

'# As a first step we normalize the user vectors to unit vectors.'

magnitude = np.sqrt(np.square(data_items).sum(axis=1))
data_items = data_items.divide(magnitude, axis='index')

'#print(data_items.head(5))'


def calculate_similarity(data_items):
    data_sparse = sparse.csr_matrix(data_items)
    similarities = cosine_similarity(data_sparse.transpose())
    '#print(similarities)'
    sim = pd.DataFrame(data=similarities, index=data_items.columns, columns=data_items.columns)
    return sim

'# Build the similarity matrix'
data_matrix = calculate_similarity(data_items)
'#print(data_matrix.head())'

#''prints the item-item similarity matrix for all items in DATASET, for example:'
#                      Acoustic Blues     ...      West Coast
# Acoustic Blues             1.000000     ...        0.000000
# African Music              0.044191     ...        0.005636
# Alternative                0.008042     ...        0.028171
# Alternative Country        0.037340     ...        0.011230
# Asian Music                0.000000     ...        0.004623


print('\n\n#########################################INTEREST GROUPS###############################################\n\n')


for i in interest_group_centroids:
    Interest_group = data_matrix.loc[i].nlargest(p).index.values
    print('Interest group', interest_group_centroids.index(i), ' = ', Interest_group, '\n')
    interest_groups.append(Interest_group)
'#print(interest_groups)'

print('\n\n#######################FREQUENT-ITEMSETS_APRIORI#######################################################\n\n')


d = apriori(df, min_support=0.2, use_colnames=True, max_len=2)
print((d['itemsets']))


print('#############################################USERS & THEIR LIKES###########################################\n\n')


user = [41000]                                         # The id of the user for whom we want to generate recommendations
user_index = data[data.user == user].index.tolist()[0] # Get the frame index
#print('user index is: ', user_index)'
known_user_likes = data_items.ix[user_index]
known_user_likes = known_user_likes[known_user_likes > 0].index.values
print('user', user_index, 'likes', known_user_likes, '\n')


print('#############################################USERS ASSOCIATION TO INTEREST GROUPS##########################\n\n')


# for i in interest_groups:
#     a_vals = Counter(i)
#     b_vals = Counter(known_user_likes)
#
#     # convert to word-vectors, for Example:
#     # [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
#     # [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#     # [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
#     # [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#
#     words = list(a_vals.keys() | b_vals.keys())
#     a_vect = [a_vals.get(word, 0) for word in words]
#     b_vect = [b_vals.get(word, 0) for word in words]
#     # find cosine
#     len_a = sum(av * av for av in a_vect) ** 0.5
#     len_b = sum(bv * bv for bv in b_vect) ** 0.5
#     dot = sum(av * bv for av, bv in zip(a_vect, b_vect))
#     cosine = dot / (len_a * len_b)
#
#     if cosine == 0:
#         pass
#     else:
#         #print('User:', user_index, 'is associated to the Interest group:', i, 'with similarity:', cosine)
#         print('')

Selected_users_association_IG = [user]

for i in interest_groups:
    interest_groups_set = set(i)
    user_likes_set = set(known_user_likes)
    sim_num = user_likes_set.intersection(interest_groups_set)
    sim_den = user_likes_set.union(interest_groups_set)
    sim = len(sim_num)/len(sim_den)

    if sim > 0:
        g = 'User:', user_index, 'is associated to the Interest group:', i, 'with similarity:', sim
        ass_interest_groups = i
        Selected_users_association_IG.append(ass_interest_groups.tolist())

print(Selected_users_association_IG[1])


#user_likes_set.intersection(interest_groups_set)

print('\n\n#########################################CLIENT_SIDE_RECOMMENDATIONS###################################\n\n')

left = []
right = []

for i in range(0, len(d['itemsets'])):
    f_s = d['itemsets'][i]
    #print('Recommendation', i, 'is: ', f_s)
    LHS = f_s
    RHS = f_s
    l, *_ = LHS
    *_, r = RHS
    print(l)
    left.append(l)
    right.append(r)
for index in range(1, len(Selected_users_association_IG)):
    if l in set(Selected_users_association_IG[index]):
         #print(l,'exist')# LHS in user and if LHS present recommend
        if l in set(known_user_likes):
            print('user', user_index, 'gets recommendation:', r)



    #print('Items to be checked in users list', l, '\n')
    #print('If item', l, 'is present', 'recommend: ', r, '\n')