-
Notifications
You must be signed in to change notification settings - Fork 0
/
app.py
296 lines (257 loc) · 9.62 KB
/
app.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
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
#server file which handels all the requests that come in
from numpy import *
import numpy as np
from scipy import optimize #used to minimize the cost function
from flask import Flask, render_template, request, redirect
from flask_mysqldb import MySQL
import yaml
#render_template is used to render HTML file
app=Flask(__name__)
#configure db
db=yaml.load(open('db.yaml'))
app.config['MYSQL_HOST']=db['mysql_host']
app.config['MYSQL_USER']=db['mysql_user']
app.config['MYSQL_PASSWORD']=db['mysql_password']
app.config['MYSQL_DB']=db['mysql_db']
mysql=MySQL(app)
def normalize_rat(ratings,did_rate):
num_places=ratings.shape[0]
num_users=ratings.shape[1]
ratings_mean=zeros(shape=(num_places,1))
ratings_norm=zeros(shape=ratings.shape)
for i in range(num_places):
c=0
place=[]
for j in range(num_users):
if did_rate[i][j]==1:
ratings_mean[i]+=ratings[i][j]
c+=1
ratings_mean[i]/=c
if c==0:
ratings_mean[i]=0
for j in range(num_users):
if did_rate[i][j]==1:
ratings_norm[i][j]=ratings[i][j]-ratings_mean[i]
return ratings_norm, ratings_mean
def normalize(place_features):
num_places=place_features.shape[0]
ratings_mean=zeros(shape=(num_places,1))
ratings_norm=zeros(shape=place_features.shape)
for i in range(num_places):
ratings_mean[i]=mean(place_features[i])
ratings_norm[i]=place_features[i]-ratings_mean[i]
return ratings_norm,ratings_mean
def unroll_params(X_and_theta, num_users, num_places, num_features):
# Retrieve the X and theta matrixes from X_and_theta, based on their dimensions (num_features, num_places, num_places)
# --------------------------------------------------------------------------------------------------------------
# Get the first 30 (10 * 3) rows in the 48 X 1 column vector
first_30 = X_and_theta[:num_places * num_features]
# Reshape this column vector into a 10 X 3 matrix
X = first_30.reshape((num_features, num_places)).transpose()
# Get the rest of the 18 the numbers, after the first 30
last_18 = X_and_theta[num_places * num_features:]
# Reshape this column vector into a 6 X 3 matrix
theta = last_18.reshape(num_features, num_users ).transpose()
return X, theta
def calculate_gradient(X_and_theta, ratings, did_rate, num_users, num_places, num_features, reg_param):
X, theta = unroll_params(X_and_theta, num_users, num_places, num_features)
# we multiply by did_rate because we only want to consider observations for which a rating was given
difference = X.dot( theta.T ) * did_rate - ratings
X_grad = difference.dot( theta ) + reg_param * X
theta_grad = difference.T.dot( X ) + reg_param * theta
# wrap the gradients back into a column vector
return r_[X_grad.T.flatten(), theta_grad.T.flatten()]
def calculate_cost(X_and_theta,ratings,did_rate,num_users,num_places,num_features,reg_param):
X, theta = unroll_params(X_and_theta, num_users, num_places, num_features)
# we multiply (element-wise) by did_rate because we only want to consider observations for which a rating was given
#we are finding the errors in this
cost = sum( (X.dot( theta.T ) * did_rate - ratings) ** 2 ) / 2
# '**' means an element-wise power
#regularize to overcome overfitting
regularization = (reg_param / 2) * (sum( theta**2 ) + sum(X**2))
return cost + regularization
@app.route('/recommender')
def recommender():
cur=mysql.connection.cursor()
num_places=48
reg_param=30 #regularisation function
#contains the ratings given by the users to different places
cur.execute("SELECT * FROM ratings")
rate=cur.fetchall()
num=len(rate)
ratings=[[0 for i in range(num)] for j in range(48)]
#transpose the matrix
for i in range(num):
for j in range(48):
ratings[j][i]=rate[i][j]
num_users=num
#did_rate checks if a user has rated a places
did_rate=[[0 for i in range(num_users)] for j in range(num_places)]
for i in range(num_places):
for j in range(num_users):
if ratings[i][j]!=0:
did_rate[i][j]=1
ratings=np.array(ratings)
#print(did_rate)
#print()
#return str(ratings)
#Normalize our data
#Normalization makes the average of the data as a 0
ratings,ratings_mean = normalize_rat(ratings,did_rate)
#return str(ratings_mean)
#print(ratings)
#update the number of users
num_users=ratings.shape[1]
num_features=13
#how much of each feature is present
cur.execute("SELECT * FROM place_features")
place_features=cur.fetchall()
place_features=np.array(place_features)
place_features,place_mean=normalize(place_features)
#print('place_features')
#print(place_features)
#print()
#return str(place_features)
#what kind of place a user would prefer
cur.execute("SELECT * FROM user_prefs")
user_prefs=cur.fetchall()
user_prefs=np.array(user_prefs)
user_prefs,user_mean=normalize(user_prefs)
#print("user_prefs")
#print(user_prefs)
#return str(user_prefs)
#X=place features and theta=user pref y=X*theta
initial_X_and_theta=r_[place_features.T.flatten(),user_prefs.T.flatten()]
#return str(initial_X_and_theta)
#we are going to use gradient descent
#performing gradient descent
minimized_cost_and_optimal_params = optimize.fmin_cg(calculate_cost, fprime=calculate_gradient, x0=initial_X_and_theta, args=(ratings, did_rate, num_users, num_places, num_features, reg_param),maxiter=100, disp=True, full_output=True )
#return str(minimized_cost_and_optimal_params)
cost, optimal_place_features_and_user_prefs = minimized_cost_and_optimal_params[1], minimized_cost_and_optimal_params[0]
#return str(optimal_place_features_and_user_prefs)
place_features, user_prefs = unroll_params(optimal_place_features_and_user_prefs, num_users, num_places, num_features)
#print(place_features)
#return str(place_features)
all_predictions = place_features.dot( user_prefs.T )
#return str(ratings_mean)
predictions_for_user=all_predictions[:,0:1]+ratings_mean
#print("Final ratings I would give to the Places:")
#return str(all_predictions)
final_output=[]
for i in range(num_places):
final_output.append([predictions_for_user[i],i+1])
final_output.sort(reverse=True)
#return str(final_output[1][0])
cur.execute("SELECT * FROM place_id")
place_id=cur.fetchall()
cur.execute("DROP TABLE results")
cur.execute("CREATE TABLE results (place VARCHAR(32))")
#return str(final_output)
for i in range(10):
cur.execute("SELECT place from place_id WHERE id ="+str(final_output[i][1]))
#return str(final_output[i][1])
ans=cur.fetchone()
#return str(ans)
m=str(ans[0])
#return str(m)
cur.execute("INSERT INTO results (place) VALUES(%s)",[str(m)])
#return str(pl)
#print(final_output[i][0]," ",final_output[i][1])
#print(predictions_for_user)
#print(len(predictions_for_user))
cur.execute("SELECT * FROM results")
userDetails=cur.fetchall()
mysql.connection.commit()
cur.close()
return render_template('users.html',userDetails=userDetails)
@app.route('/ratings',methods=['GET','POST'])
def ratings():
if request.method=='POST':
userDetails=request.form
r1=userDetails['n1']
r2=userDetails['n2']
r3=userDetails['n3']
r4=userDetails['n4']
r5=userDetails['n5']
r6=userDetails['n6']
r7=userDetails['n7']
r8=userDetails['n8']
r9=userDetails['n9']
r10=userDetails['n10']
r11=userDetails['n11']
r12=userDetails['n12']
r13=userDetails['n13']
r14=userDetails['n14']
r15=userDetails['n15']
r16=userDetails['n16']
r17=userDetails['n17']
r18=userDetails['n18']
r19=userDetails['n19']
r20=userDetails['n20']
r21=userDetails['n21']
r22=userDetails['n22']
r23=userDetails['n23']
r24=userDetails['n24']
r25=userDetails['n25']
r26=userDetails['n26']
r27=userDetails['n27']
r28=userDetails['n28']
r29=userDetails['n29']
r30=userDetails['n30']
r31=userDetails['n31']
r32=userDetails['n32']
r33=userDetails['n33']
r34=userDetails['n34']
r35=userDetails['n35']
r36=userDetails['n36']
r37=userDetails['n37']
r38=userDetails['n38']
r39=userDetails['n39']
r40=userDetails['n40']
r41=userDetails['n41']
r42=userDetails['n42']
r43=userDetails['n43']
r44=userDetails['n44']
r45=userDetails['n45']
r46=userDetails['n46']
r47=userDetails['n47']
r48=userDetails['n48']
cur=mysql.connection.cursor()
cur.execute("INSERT INTO ratings (n1,n2,n3,n4,n5,n6,n7,n8,n9,n10,n11,n12,n13,n14,n15,n16,n17,n18,n19,n20,n21,n22,n23,n24,n25,n26,n27,n28,n29,n30,n31,n32,n33,n34,n35,n36,n37,n38,n39,n40,n41,n42,n43,n44,n45,n46,n47,n48) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)",(r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22,r23,r24,r25,r26,r27,r28,r29,r30,r31,r32,r33,r34,r35,r36,r37,r38,r39,r40,r41,r42,r43,r44,r45,r46,r47,r48))
mysql.connection.commit()
cur.close()
return redirect('/recommender')
#return 'success'
return render_template('rat.html')
@app.route('/userprefs',methods=['GET','POST'])
def userprefs():
if request.method=='POST':
#Fetch the form data
userDetails=request.form
p1=userDetails['n1']
p2=userDetails['n2']
p3=userDetails['n3']
p4=userDetails['n4']
p5=userDetails['n5']
p6=userDetails['n6']
p7=userDetails['n7']
p8=userDetails['n8']
p9=userDetails['n9']
p10=userDetails['n10']
p11=userDetails['n11']
p12=userDetails['n12']
p13=userDetails['n13']
cur=mysql.connection.cursor()
cur.execute("INSERT INTO user_prefs (k1,k2,k3,k4,k5,k6,k7,k8,k9,k10,k11,k12,k13) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)",(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13))
mysql.connection.commit()
cur.close()
return redirect('/ratings')
#return 'success'
return render_template('keyword.html')
@app.route('/',methods=['GET','POST'])
def home():
if request.method=='POST':
return redirect('/userprefs')
return render_template('website.html')
if __name__=='__main__':
app.run(debug=True)