-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_STH.py
194 lines (153 loc) · 6.79 KB
/
train_STH.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
import os
import numpy as np
import pandas as pd
from scipy import sparse
from scipy.sparse import csr_matrix, csc_matrix
from tqdm import tqdm
import scipy
from scipy.sparse.linalg import eigsh
from scipy.sparse import coo_matrix
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.svm import LinearSVC
from utils import *
import argparse
from datasets import *
##################################################################################################
parser = argparse.ArgumentParser()
parser.add_argument("-g", "--gpunum", help="GPU number to train the model.")
parser.add_argument("-d", "--dataset", help="Name of the dataset.")
parser.add_argument('--save_hash_code', dest='save_hash_code', action='store_true')
parser.set_defaults(save_hash_code=False)
##################################################################################################
args = parser.parse_args()
if not args.gpunum:
parser.error("Need to provide the GPU number.")
if not args.dataset:
parser.error("Need to provide the dataset.")
##################################################################################################
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpunum
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#########################################################################################################
dataset_name = args.dataset
if dataset_name in ['reuters', 'tmc', 'rcv1', 'dblp']:
single_label = False
else:
single_label = True
#########################################################################################################
max_nodes = 20
gpunum = args.gpunum
# fn = os.path.join(data_dir, 'train.NN.pkl')
# df_train = pd.read_pickle(fn)
# #df_train.set_index('doc_id', inplace=True)
# docid2index = {docid: index for index, docid in enumerate(list(df_train.index))}
# # Test data
# fn = os.path.join(data_dir, 'test.NN.pkl')
# df_test = pd.read_pickle(fn)
# #df_test.set_index('doc_id', inplace=True)
data_dir = os.path.join('dataset/clean', dataset_name)
train_set = TextDataset(dataset_name, data_dir, subset='train')
test_set = TextDataset(dataset_name, data_dir, subset='test')
train_loader = torch.utils.data.DataLoader(dataset=train_set, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=128, shuffle=True)
num_train = len(train_set)
num_test = len(test_set)
from scipy import sparse
from scipy.sparse import csr_matrix, csc_matrix
docid2index = {docid: index for index, docid in enumerate(list(train_set.df.index))}
r = []
c = []
row_index = 0
for idx, row in train_set.df.iterrows():
col = [docid2index[docid] for docid in train_set.df.neighbors.iloc[idx].nonzero()[1]]
r += [row_index] * len(col)
c += col
row_index += 1
d = [0.9] * len(c)
weight_mat = csc_matrix((d, (r, c)), shape=(num_train, num_train))
train_bow = sparse.vstack(list(train_set.df.bow))
#test_bow = sparse.vstack(list(test_set.df.bow))
weight_mat = csc_matrix((d, (r, c)), shape=(num_train, num_train))
train_bow = sparse.vstack(list(train_set.df.bow))
test_bow = sparse.vstack(list(test_set.df.bow))
class MedianHashing(object):
def __init__(self):
self.threshold = None
self.latent_dim = None
def fit(self, X):
self.threshold = np.median(X, axis=0)
self.latent_dim = X.shape[1]
def transform(self, X):
assert(X.shape[1] == self.latent_dim)
binary_code = np.zeros(X.shape)
for i in range(self.latent_dim):
binary_code[np.nonzero(X[:,i] < self.threshold[i]),i] = 0
binary_code[np.nonzero(X[:,i] >= self.threshold[i]),i] = 1
return binary_code.astype(int)
def fit_transform(self, X):
self.fit(X)
return self.transform(X)
class STH:
def __init__(self, num_bits, topK):
super(STH, self).__init__()
self.num_bits = num_bits
self.clfs = [LinearSVC() for n in range(num_bits)]
self.topK = topK
def fit_transform(self, bow_mat, weight_mat, num_train):
W = weight_mat
D = np.asarray(W.sum(axis=1)).squeeze() + 0.0001 # adding damping value for a numerical stabability
D = scipy.sparse.diags(D)
L = D - W
L = scipy.sparse.csc_matrix(L)
D = scipy.sparse.csc_matrix(D)
num_attempts = 0
max_attempts = 3
success = False
while not success:
E, Y = eigsh(L, k=self.num_bits+1, M=D, which='SM')
success = np.all(np.isreal(Y))
if not success:
print("Warning: Some eigenvalues are not real values. Retry to solve Eigen-decomposition.")
num_attempts += 1
if num_attempts > max_attempts:
assert(np.all(np.isreal(Y))) # if this fails, re-run fit again
assert(False) # Check your data
Y = np.real(Y)
Y = Y[:, 1:]
medHash = MedianHashing()
cbTrain = medHash.fit_transform(Y)
for b in range(0, cbTrain.shape[1]):
self.clfs[b].fit(bow_mat, cbTrain[:, b])
return cbTrain
def transform(self, bow_mat, num_test):
cbTest = np.zeros((num_test, self.num_bits), dtype=np.int64)
for b in range(0, self.num_bits):
cbTest[:,b] = self.clfs[b].predict(bow_mat)
return cbTest
os.environ["CUDA_VISIBLE_DEVICES"]=gpunum
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
train_y = torch.from_numpy(sparse.vstack(list(train_set.df.label)).toarray())
test_y = torch.from_numpy(sparse.vstack(list(test_set.df.label)).toarray())
assert(train_y.size(1) == test_y.size(1))
with torch.no_grad():
prec_results = []
for num_bits in [8, 16, 32, 64, 128]:
print('train STH with {} bits ...'.format(num_bits))
model = STH(num_bits, None)
train_b = model.fit_transform(train_bow, weight_mat, None)
test_b = model.transform(test_bow, test_bow.shape[0])
# convert hash to Tensor
train_b = torch.Tensor(list(train_b)).type(torch.ByteTensor)
test_b = torch.Tensor(list(test_b)).type(torch.ByteTensor)
assert(train_b.size(0) == train_y.size(0))
assert(test_b.size(0) == test_y.size(0))
assert(train_b.size(1) == test_b.size(1))
print("Evaluating the binary codes ...")
retrieved_indices = retrieve_topk(test_b.to(device), train_b.to(device), topK=100)
prec = compute_precision_at_k(retrieved_indices, test_y.to(device), train_y.to(device), topK=100)
print("bit:{} precision at 100: {:.4f}".format(num_bits, prec.item()))
prec_results.append(prec.item())
del train_b
del test_b
torch.cuda.empty_cache()
result = ' & '.join(['{:.4f}'.format(p) for p in prec_results])
print(result)