-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsample_demo.py
134 lines (111 loc) · 4.19 KB
/
sample_demo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 27 19:35:35 2018
@author: J N BALAKUMARAN
"""
from recommendation_data import dataset
from math import sqrt
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
def pearson_correlation(person1,person2):
# To get both rated items
both_rated = {}
for item in dataset[person1]:
if item in dataset[person2]:
both_rated[item] = 1
number_of_ratings = len(both_rated)
# Checking for number of ratings in common
if number_of_ratings == 0:
return 0
# Add up all the preferences of each user
person1_preferences_sum = sum([dataset[person1][item] for item in both_rated])
person2_preferences_sum = sum([dataset[person2][item] for item in both_rated])
# Sum up the squares of preferences of each user
person1_square_preferences_sum = sum([pow(dataset[person1][item],2) for item in both_rated])
person2_square_preferences_sum = sum([pow(dataset[person2][item],2) for item in both_rated])
# Sum up the product value of both preferences for each item
product_sum_of_both_users = sum([dataset[person1][item] * dataset[person2][item] for item in both_rated])
# Calculate the pearson score
numerator_value = product_sum_of_both_users - (person1_preferences_sum*person2_preferences_sum/number_of_ratings)
denominator_value = sqrt((person1_square_preferences_sum - pow(person1_preferences_sum,2)/number_of_ratings) * (person2_square_preferences_sum -pow(person2_preferences_sum,2)/number_of_ratings))
if denominator_value == 0:
return 0
else:
r = numerator_value/denominator_value
return r
def most_similar_users(person,number_of_users):
# returns the number_of_users (similar persons) for a given specific person.
scores = [(pearson_correlation(person,other_person),other_person) for other_person in dataset if other_person != person ]
# Sort the similar persons so that highest scores person will appear at the first
scores.sort()
scores.reverse()
return scores[0:number_of_users]
def user_recommendations(person):
# Gets recommendations for a person by using a weighted average of every other user's rankings
totals = {}
simSums = {}
rankings_list =[]
for other in dataset:
# don't compare me to myself
if other == person:
continue
sim = pearson_correlation(person,other)
#print (">>>>>>>",sim)
# ignore scores of zero or lower
if sim <=0:
continue
for item in dataset[other]:
# only score movies i haven't seen yet
if item not in dataset[person] or dataset[person][item] == 0:
# Similrity * score
totals.setdefault(item,0)
totals[item] += dataset[other][item]* sim
# sum of similarities
simSums.setdefault(item,0)
simSums[item]+= sim
# Create the normalized list
rankings = [(total/simSums[item],item) for item,total in totals.items()]
rankings.sort()
rankings.reverse()
#print(rankings)
# returns the recommended items
recommendataions_list = [(recommend_item,score) for score,recommend_item in rankings]
return recommendataions_list
#scores=most_similar_users('student',5)
#print(scores)
new=user_recommendations('student')
dataset["student"].update(new)
dataset['student']['ds']=round(dataset['student']['ds'],1)
#print(dataset)
new=user_recommendations('industry')
dataset["industry"].update(new)
dataset['industry']['stat']=round(dataset['industry']['stat'],1)
print(dataset)
csa=ppl=mfcs=ds=stat=0
for key in dataset:
ppl+=dataset[key]['ppl']
csa+=dataset[key]['csa']
stat+=dataset[key]['stat']
mfcs+=dataset[key]['mfcs']
ds+=dataset[key]['ds']
print("ppl:",ppl,"csa:",csa,"stat:",stat,"mfcs:",mfcs,"ds:",ds)
mean=[]
print("length of dataset",len(dataset))
mean.append(ppl/len(dataset))
mean.append(csa/len(dataset))
mean.append(stat/len(dataset))
mean.append(mfcs/len(dataset))
mean.append(ds/len(dataset))
print(mean)
mean.sort()
mean.reverse()
print(mean)
objects = ('ppl','stat','csa','ds','mfcs')
y_pos = np.arange(len(objects))
plt.bar(y_pos, mean, align='center', alpha=0.55)
plt.xticks(y_pos, objects)
plt.ylabel('rating')
plt.xlabel('courses')
plt.title('overall rating')
plt.show()