-
Notifications
You must be signed in to change notification settings - Fork 1
/
classifier.py
265 lines (208 loc) · 10.7 KB
/
classifier.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
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 20 02:13:41 2019
@author: islam
"""
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, roc_auc_score,f1_score,recall_score
import heapq # for retrieval topK
from utilities import get_instances_with_random_neg_samples, get_test_instances_with_random_samples
from performance_and_fairness_measures import getHitRatio, getNDCG, differentialFairnessMultiClass, computeEDF_clf, computeAbsoluteUnfairness_clf
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
from collaborative_models import neuralClassifier
#%%The function below ensures that we seed all random generators with the same value to get reproducible results
def set_random_seed(state=1):
gens = (np.random.seed, torch.manual_seed, torch.cuda.manual_seed)
for set_state in gens:
set_state(state)
RANDOM_STATE = 1
set_random_seed(RANDOM_STATE)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#%% loss function for differential fairness
def criterionHinge(epsilonClass, epsilonBase):
zeroTerm = torch.tensor(0.0).to(device)
return torch.max(zeroTerm, (epsilonClass-epsilonBase))
#%% fine-tuning pre-trained model with user-career pairs
def fair_fine_tune_model(model,df_train, epochs, lr,batch_size,num_negatives,num_items,protectedAttributes,lamda,epsilonBase,unsqueeze=False):
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-6)
model.train()
all_user_input = torch.LongTensor(df_train['user_id'].values).to(device)
all_item_input = torch.LongTensor(df_train['like_id'].values).to(device)
for i in range(epochs):
j = 0
for batch_i in range(0,np.int64(np.floor(len(df_train)/batch_size))*batch_size,batch_size):
data_batch = (df_train[batch_i:(batch_i+batch_size)]).reset_index(drop=True)
train_user_input, train_item_input, train_ratings = get_instances_with_random_neg_samples(data_batch, num_items, num_negatives,device)
if unsqueeze:
train_ratings = train_ratings.unsqueeze(1)
y_hat = model(train_user_input, train_item_input)
loss1 = criterion(y_hat, train_ratings)
predicted_probs = model(all_user_input, all_item_input)
avg_epsilon = computeEDF(protectedAttributes,predicted_probs,num_items,all_item_input,device)
loss2 = criterionHinge(avg_epsilon, epsilonBase)
loss = loss1 + lamda*loss2
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch: ', i, 'batch: ', j, 'out of: ',np.int64(np.floor(len(df_train)/batch_size)), 'average loss: ',loss.item())
j = j+1
#%% model evaluation
def test_fine_tune(model,df_val,num_negatives,num_items, unsqueeze=False):
model.eval()
test_user_input, test_item_input, test_ratings= get_instances_with_random_neg_samples(df_val, num_items, num_negatives,device)
if unsqueeze:
test_ratings = test_ratings.unsqueeze(1)
y_hat = model(test_user_input, test_item_input)
predicted_ratings = (((y_hat.cpu())>0.5).numpy()).reshape((-1,))
y_hat = y_hat.cpu().detach().numpy().reshape((-1,))
test_ratings = test_ratings.cpu().detach().numpy().reshape((-1,))
Accuracy = sum(predicted_ratings == test_ratings)/len(test_ratings)
print(f"accuracy: {Accuracy: .3f}")
aucScore = roc_auc_score(test_ratings,y_hat)
print(f"ROC AUC: {aucScore: .3f}")
f1_measure = f1_score(test_ratings,predicted_ratings)
print(f"F1 score: {f1_measure: .2f}")
recall_measure = recall_score(test_ratings,predicted_ratings)
print(f"Recall score: {recall_measure: .2f}")
#%% model evaluation: hit rate and NDCG
def evaluate_fine_tune(model,df_val,top_K,random_samples, num_items):
model.eval()
avg_HR = np.zeros((len(df_val),top_K))
avg_NDCG = np.zeros((len(df_val),top_K))
for i in range(len(df_val)):
test_user_input, test_item_input = get_test_instances_with_random_samples(df_val[i], random_samples,num_items,device)
y_hat = model(test_user_input, test_item_input)
y_hat = y_hat.cpu().detach().numpy().reshape((-1,))
test_item_input = test_item_input.cpu().detach().numpy().reshape((-1,))
map_item_score = {}
for j in range(len(y_hat)):
map_item_score[test_item_input[j]] = y_hat[j]
for k in range(top_K):
# Evaluate top rank list
ranklist = heapq.nlargest(k, map_item_score, key=map_item_score.get)
gtItem = test_item_input[0]
avg_HR[i,k] = getHitRatio(ranklist, gtItem)
avg_NDCG[i,k] = getNDCG(ranklist, gtItem)
avg_HR = np.mean(avg_HR, axis = 0)
avg_NDCG = np.mean(avg_NDCG, axis = 0)
return avg_HR, avg_NDCG
#%%
def fairness_measures(model,df_val,num_items,protectedAttributes):
model.eval()
user_input = torch.LongTensor(df_val['user_id'].values).to(device)
item_input = torch.LongTensor(df_val['like_id'].values).to(device)
y_hat = model(user_input, item_input)
avg_epsilon = computeEDF(protectedAttributes,y_hat,num_items,item_input,device)
U_abs = computeAbsoluteUnfairness(protectedAttributes,y_hat,num_items,item_input,device)
avg_epsilon = avg_epsilon.cpu().detach().numpy().reshape((-1,)).item()
print(f"average differential fairness: {avg_epsilon: .3f}")
U_abs = U_abs.cpu().detach().numpy().reshape((-1,)).item()
print(f"absolute unfairness: {U_abs: .3f}")
#%% load data
train_users= pd.read_csv("train-test/train_usersID.csv",names=['user_id'])
test_users = pd.read_csv("train-test/test_usersID.csv",names=['user_id'])
train_careers= pd.read_csv("train-test/train_concentrationsID.csv",names=['like_id'])
test_careers = pd.read_csv("train-test/test_concentrationsID.csv",names=['like_id'])
train_protected_attributes= pd.read_csv("train-test/train_protectedAttributes.csv")
test_protected_attributes = pd.read_csv("train-test/test_protectedAttributes.csv")
# =============================================================================
# train_labels= pd.read_csv("train-test/train_labels.csv",names=['labels'])
# test_labels = pd.read_csv("train-test/test_labels.csv",names=['labels'])
#
# unique_concentrations = (pd.concat([train_careers['like_id'],train_labels['labels']],axis=1)).reset_index(drop=True)
# unique_concentrations = unique_concentrations.drop_duplicates(subset='like_id', keep='first')
#
# unique_careers = unique_concentrations.sort_values(by=['like_id']).reset_index(drop=True)
# unique_careers.to_csv('train-test/unique_careers.csv',index=False)
# =============================================================================
unique_careers= pd.read_csv("train-test/unique_careers.csv")
train_userPages = pd.read_csv("train-test/train_userPages.csv")
num_uniqueUsers = len(train_userPages.user_id.unique())
num_uniqueLikes = len(train_userPages.like_id.unique())
# to fine tune career recommendation
num_uniqueCareers = len(train_careers.like_id.unique())
# form binary features
usr_features = np.zeros((num_uniqueUsers,num_uniqueLikes))
for i in range(len(train_userPages)):
usr_features[train_userPages['user_id'][i],train_userPages['like_id'][i]] = 1.0
train_features = np.zeros((len(train_users),num_uniqueLikes))
test_features = np.zeros((len(test_users),num_uniqueLikes))
for i in range(len(train_users)):
train_features[i] = usr_features[train_users['user_id'][i]]
for i in range(len(test_users)):
test_features[i] = usr_features[test_users['user_id'][i]]
from sklearn.decomposition import PCA,IncrementalPCA
pca = IncrementalPCA(n_components=50,batch_size=64)
pca.fit(train_features)
train_features = pca.transform(train_features)
test_features = pca.transform(test_features)
#%% set hyperparameters
hidden_layers = np.array([train_features.shape[1], 64, 32, 16])
output_size = num_uniqueCareers
num_epochs = 200
learning_rate = 0.001
batch_size = 256
random_samples = 15
top_K = 10
train_gender = train_protected_attributes['gender'].values
test_gender = test_protected_attributes['gender'].values
#%% clf model
trainData = torch.from_numpy(train_features)
trainLabel = torch.from_numpy(train_careers.values)
testData = torch.from_numpy(test_features)
clf_model = neuralClassifier(train_features.shape[1], hidden_layers,output_size)
#%% training
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(clf_model.parameters(), lr=learning_rate, weight_decay=1e-6)
clf_model.train()
trainData = Variable(trainData.float())
trainLabel = Variable(trainLabel.squeeze(1).long())
for i in range(num_epochs):
for batch_i in range(0,np.int64(np.floor(len(trainData)/batch_size))*batch_size,batch_size):
train_batch = trainData[batch_i:batch_i+batch_size,:]
label_batch = trainLabel[batch_i:batch_i+batch_size]
y_hat = clf_model(train_batch)
loss = criterion(y_hat, label_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch: ', i, 'average loss: ',loss.item())
#%% evaluate
testData = Variable(testData.float())
with torch.no_grad():
avg_HR = np.zeros((len(test_features),top_K))
avg_NDCG = np.zeros((len(test_features),top_K))
for i in range(len(test_features)):
y_hat = clf_model(testData[i])
# _, predicted = torch.max(y_hat.data, 0)
for ki in range(top_K):
# Evaluate top rank list
idx = torch.topk(y_hat.data, k=ki, dim=0)[1]
ranklist = idx.tolist()
gtItem = test_careers['like_id'][i]
avg_HR[i,ki] = getHitRatio(ranklist, gtItem)
avg_NDCG[i,ki] = getNDCG(ranklist, gtItem)
avg_HR = np.mean(avg_HR, axis = 0)
avg_NDCG = np.mean(avg_NDCG, axis = 0)
np.savetxt('results/avg_HR_CLF.txt',avg_HR)
np.savetxt('results/avg_NDCG_CLF.txt',avg_NDCG)
#%% evaluate fairness
import sys
sys.stdout=open("dnnClf_output.txt","w")
with torch.no_grad():
y_hat = clf_model(testData)
device = torch.device("cpu")
y_hat = torch.nn.functional.softmax(y_hat,dim=1)
item_input = test_careers['like_id'].values
avg_epsilon = computeEDF_clf(test_gender,y_hat,num_uniqueCareers,item_input,device)
U_abs = computeAbsoluteUnfairness_clf(test_gender,y_hat,num_uniqueCareers,item_input,device)
avg_epsilon = avg_epsilon.numpy().reshape((-1,)).item()
print(f"average differential fairness: {avg_epsilon: .3f}")
U_abs = U_abs.numpy().reshape((-1,)).item()
print(f"absolute unfairness: {U_abs: .3f}")