-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmySSVAE.py
343 lines (213 loc) · 14.6 KB
/
mySSVAE.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
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
from __future__ import print_function
import numpy as np
import tensorflow as tf
class Model(object):
def __init__(self, seqlen_x, dim_x, dim_y, dim_z=100, dim_h=250, n_hidden=3, batch_size=200, beta=10000., char_set=[' ']):
self.seqlen_x, self.dim_x, self.dim_y, self.dim_z, self.dim_h, self.n_hidden, self.batch_size = seqlen_x, dim_x, dim_y, dim_z, dim_h, n_hidden, batch_size
self.beta = beta
self.char_to_int = dict((c,i) for i,c in enumerate(char_set))
self.int_to_char = dict((i,c) for i,c in enumerate(char_set))
self.G = tf.Graph()
self.G.as_default()
## variables for labeled data
self.x_L = tf.placeholder(tf.float32, [None, self.seqlen_x, self.dim_x])
self.xs_L = tf.placeholder(tf.float32, [None, self.seqlen_x, self.dim_x])
self.y_L = tf.placeholder(tf.float32, [None, self.dim_y])
## functions for labeled data
self.classifier_L_out = self._rnnpredictor(self.x_L, self.dim_h, 2*self.dim_y, reuse = False)
self.y_L_mu, self.y_L_lsgms = tf.split(self.classifier_L_out, [self.dim_y, self.dim_y], 1)
self.y_L_sample = self._draw_sample(self.y_L_mu, self.y_L_lsgms)
self.encoder_L_out = self._rnnencoder(self.x_L, self.y_L, self.dim_h, 2*self.dim_z, reuse = False)
self.z_L_mu, self.z_L_lsgms = tf.split(self.encoder_L_out, [self.dim_z, self.dim_z], 1)
self.z_L_sample = self._draw_sample(self.z_L_mu, self.z_L_lsgms)
self.decoder_L_out = self._rnndecoder(self.xs_L, tf.concat([self.z_L_sample, self.y_L], 1), self.dim_h, self.dim_x, reuse = False)
self.x_L_recon = tf.nn.softmax(self.decoder_L_out)
self.decoder_DL_out = self._rnndecoder(self.xs_L, tf.concat([self.z_L_mu, self.y_L], 1), self.dim_h, self.dim_x, reuse = True)
self.x_DL_recon = tf.nn.softmax(self.decoder_DL_out)
self.z_G = tf.placeholder(tf.float32, [None, dim_z])
self.decoder_G_out = self._rnndecoder(self.xs_L, tf.concat([self.z_G, self.y_L], 1), self.dim_h, self.dim_x, reuse = True)
self.x_G_recon = tf.nn.softmax(self.decoder_G_out)
## variables for unlabeled data
self.x_U = tf.placeholder(tf.float32, [None, self.seqlen_x, self.dim_x])
self.xs_U = tf.placeholder(tf.float32, [None, self.seqlen_x, self.dim_x])
## functions for unlabeled data
self.classifier_U_out = self._rnnpredictor(self.x_U, self.dim_h, 2*self.dim_y, reuse = True)
self.y_U_mu, self.y_U_lsgms = tf.split(self.classifier_U_out, [self.dim_y, self.dim_y], 1)
self.y_U_sample = self._draw_sample(self.y_U_mu, self.y_U_lsgms)
self.encoder_U_out = self._rnnencoder(self.x_U, self.y_U_sample, self.dim_h, 2*self.dim_z, reuse = True)
self.z_U_mu, self.z_U_lsgms = tf.split(self.encoder_U_out, [self.dim_z, self.dim_z], 1)
self.z_U_sample = self._draw_sample(self.z_U_mu, self.z_U_lsgms)
self.decoder_U_out = self._rnndecoder(self.xs_U, tf.concat([self.z_U_sample, self.y_U_sample], 1), self.dim_h, self.dim_x, reuse = True)
self.x_U_recon = tf.nn.softmax(self.decoder_U_out)
self.encoder_U2_out = self._rnnencoder(self.x_U, self.y_U_mu, self.dim_h, 2*self.dim_z, reuse = True)
self.z_U2_mu, self.z_U2_lsgms = tf.split(self.encoder_U2_out, [self.dim_z, self.dim_z], 1)
self.decoder_DU_out = self._rnndecoder(self.xs_U, tf.concat([self.z_U2_mu, self.y_U_mu], 1), self.dim_h, self.dim_x, reuse = True)
self.x_DU_recon = tf.nn.softmax(self.decoder_DU_out)
self.saver = tf.train.Saver()
self.session = tf.Session()
def train(self, trnX_L, trnXs_L, trnY_L, trnX_U, trnXs_U, valX_L, valXs_L, valY_L, valX_U, valXs_U):
self.mu_prior=np.mean(trnY_L,0)
self.cov_prior=np.cov(trnY_L.T)
self.tf_mu_prior=tf.constant(self.mu_prior, shape=[1, self.dim_y], dtype=tf.float32)
self.tf_cov_prior=tf.constant(self.cov_prior, shape=[self.dim_y, self.dim_y], dtype=tf.float32)
# objective functions
objL = self._obj_L()
objU = self._obj_U()
objYpred_MSE = tf.reduce_mean(tf.reduce_sum(tf.squared_difference(self.y_L, self.y_L_mu), 1))
objL_val = - tf.reduce_mean(- tf.reduce_sum(self.cross_entropy(tf.layers.flatten(self.x_L), tf.layers.flatten(self.x_DL_recon)), 1))
objU_val = - tf.reduce_mean(- tf.reduce_sum(self.cross_entropy(tf.layers.flatten(self.x_U), tf.layers.flatten(self.x_DU_recon)), 1))
batch_size_L=int(self.batch_size*len(trnX_L)/(len(trnX_L)+len(trnX_U)))
batch_size_U=int(self.batch_size*len(trnX_U)/(len(trnX_L)+len(trnX_U)))
n_batch=int(len(trnX_L)/batch_size_L)
batch_size_val_L=int(len(valX_L)/10)
batch_size_val_U=int(len(valX_U)/10)
cost = (objL * float(batch_size_L) + objU * float(batch_size_U))/float(batch_size_L+batch_size_U) + float(batch_size_L)/float(batch_size_L+batch_size_U) * (self.beta * objYpred_MSE)
cost_val = objYpred_MSE
train_op = tf.train.AdamOptimizer().minimize(cost)
self.session.run(tf.global_variables_initializer())
# training
val_log=np.zeros(300)
for epoch in range(300):
[trnX_L, trnXs_L, trnY_L]=self._permutation([trnX_L, trnXs_L, trnY_L])
[trnX_U, trnXs_U]=self._permutation([trnX_U, trnXs_U])
for i in range(n_batch):
start_L=i*batch_size_L
end_L=start_L+batch_size_L
start_U=i*batch_size_U
end_U=start_U+batch_size_U
trn_res = self.session.run([train_op, cost, objL, objU, objYpred_MSE],
feed_dict = {self.x_L: trnX_L[start_L:end_L], self.xs_L: trnXs_L[start_L:end_L], self.y_L: trnY_L[start_L:end_L],
self.x_U: trnX_U[start_U:end_U], self.xs_U: trnXs_U[start_U:end_U]})
val_res = []
for i in range(10):
start_L=i*batch_size_val_L
end_L=start_L+batch_size_val_L
start_U=i*batch_size_val_U
end_U=start_U+batch_size_val_U
val_res.append(self.session.run([cost_val, objL_val, objU_val, objYpred_MSE],
feed_dict = {self.x_L: valX_L[start_L:end_L], self.xs_L: valXs_L[start_L:end_L], self.y_L: valY_L[start_L:end_L],
self.x_U: valX_U[start_U:end_U], self.xs_U: valXs_U[start_U:end_U]}))
val_res=np.mean(val_res,axis=0)
print(epoch, ['Training', 'cost_trn', trn_res[1]])
print('---', ['Validation', 'cost_val', val_res[0]])
val_log[epoch] = val_res[0]
if epoch > 20 and np.min(val_log[0:epoch-10]) * 0.99 < np.min(val_log[epoch-10:epoch+1]):
print('---termination condition is met')
break
def predict(self, x_input):
return self.session.run(self.y_U_mu, feed_dict = {self.x_U: x_input})
def latent(self, x_input, y_input):
return self.session.run(self.z_L_mu, feed_dict = {self.x_L: x_input, self.y_L: y_input})
def sampling_unconditional(self):
sample_z=np.random.randn(1, self.dim_z)
sample_y=np.random.multivariate_normal(self.mu_prior, self.cov_prior, 1)
sample_smiles=self.beam_search(sample_z, sample_y, k=5)
return sample_smiles
def mysampling_conditional(self, yid, ytarget):
def random_cond_normal(yid, ytarget):
id2=yid
id1=np.setdiff1d([0,1,2],id2)
mu1=self.mu_prior[id1]
mu2=self.mu_prior[id2]
cov11=self.cov_prior[id1][:,id1]
cov12=self.cov_prior[id1][:,id2]
cov22=self.cov_prior[id2][:,id2]
cov21=self.cov_prior[id2][:,id1]
cond_mu=np.transpose(mu1.T+np.matmul(cov12, np.linalg.inv(cov22)) * (ytarget-mu2))[0]
cond_cov=cov11 - np.matmul(np.matmul(cov12, np.linalg.inv(cov22)), cov21)
marginal_sampled=np.random.multivariate_normal(cond_mu, cond_cov, 1)
tst=np.zeros(3)
tst[id1]=marginal_sampled
tst[id2]=ytarget
return np.asarray([tst])
sample_z=np.random.randn(1, self.dim_z)
sample_y=random_cond_normal(yid, ytarget)
sample_smiles=self.beam_search(sample_z, sample_y, k=5)
return sample_smiles
def beam_search(self, z_input, y_input, k=5):
def reconstruct(xs_input, z_sample, y_input):
return self.session.run(self.x_G_recon, feed_dict = {self.xs_L: xs_input, self.z_G: z_sample, self.y_L: y_input})
cands=np.asarray([np.zeros((1, self.seqlen_x, self.dim_x), dtype=np.float32)] )
cands_score=np.asarray([100.])
for i in range(self.seqlen_x-1):
cands2=[]
cands2_score=[]
for j, samplevec in enumerate(cands):
o = reconstruct(samplevec, z_input, y_input)
sampleidxs = np.argsort(-o[0,i])[:k]
for sampleidx in sampleidxs:
samplevectt=np.copy(samplevec)
samplevectt[0, i+1, sampleidx] = 1.
cands2.append(samplevectt)
cands2_score.append(cands_score[j] * o[0,i,sampleidx])
cands2_score=np.asarray(cands2_score)
cands2=np.asarray(cands2)
kbestid = np.argsort(-cands2_score)[:k]
cands=np.copy(cands2[kbestid])
cands_score=np.copy(cands2_score[kbestid])
if np.sum([np.argmax(c[0][i+1]) for c in cands])==0:
break
sampletxt = ''.join([self.int_to_char[np.argmax(t)] for t in cands[0,0]]).strip()
return sampletxt
def _obj_L(self):
L_log_lik = - tf.reduce_sum(self.cross_entropy(tf.layers.flatten(self.x_L), tf.layers.flatten(self.x_L_recon)), 1)
L_log_prior_y = self.noniso_logpdf(self.y_L)
L_KLD_z = self.iso_KLD(self.z_L_mu, self.z_L_lsgms)
objL = - tf.reduce_mean(L_log_lik + L_log_prior_y - L_KLD_z)
return objL
def _obj_U(self):
U_log_lik = - tf.reduce_sum(self.cross_entropy(tf.layers.flatten(self.x_U), tf.layers.flatten(self.x_U_recon)), 1)
U_KLD_y = self.noniso_KLD(self.y_U_mu, self.y_U_lsgms)
U_KLD_z = self.iso_KLD(self.z_U_mu, self.z_U_lsgms)
objU = - tf.reduce_mean(U_log_lik - U_KLD_y - U_KLD_z)
return objU
def cross_entropy(self, x, y, const = 1e-10):
return - ( x*tf.log(tf.clip_by_value(y, const, 1.0))+(1.0-x)*tf.log(tf.clip_by_value(1.0-y, const, 1.0)) )
def iso_KLD(self, mu, log_sigma_sq):
return tf.reduce_sum( - 0.5 * (1.0 + log_sigma_sq - tf.square(mu) - tf.exp(log_sigma_sq) ), 1)
def noniso_logpdf(self, x):
return - 0.5 * (float(self.cov_prior.shape[0]) * np.log(2.*np.pi) + np.log(np.linalg.det(self.cov_prior))
+ tf.reduce_sum( tf.multiply( tf.matmul( tf.subtract(x, self.tf_mu_prior), tf.matrix_inverse(self.tf_cov_prior) ), tf.subtract(x, self.tf_mu_prior) ), 1) )
def noniso_KLD(self, mu, log_sigma_sq):
return 0.5 * ( tf.trace( tf.scan(lambda a, x: tf.matmul(tf.matrix_inverse(self.tf_cov_prior), x), tf.matrix_diag(tf.exp(log_sigma_sq)) ) )
+ tf.reduce_sum( tf.multiply( tf.matmul( tf.subtract(self.tf_mu_prior, mu), tf.matrix_inverse(self.tf_cov_prior) ), tf.subtract(self.tf_mu_prior, mu) ), 1)
- float(self.cov_prior.shape[0]) + np.log(np.linalg.det(self.cov_prior)) - tf.reduce_sum(log_sigma_sq, 1) )
def _permutation(self, set):
permid=np.random.permutation(len(set[0]))
for i in range(len(set)):
set[i]=set[i][permid]
return set
def _draw_sample(self, mu, lsgms):
epsilon = tf.random_normal((tf.shape(mu)), 0, 1)
sample = tf.add(mu, tf.multiply(tf.exp(0.5*lsgms), epsilon))
return sample
def _rnnpredictor(self, x, dim_h, dim_y, reuse=False):
with tf.variable_scope('rnnpredictor', reuse=reuse):
cell_fw = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(dim_h) for _ in range(self.n_hidden)])
cell_bw = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(dim_h) for _ in range(self.n_hidden)])
init_state_fw = cell_fw.zero_state(tf.shape(x)[0], tf.float32)
init_state_bw = cell_bw.zero_state(tf.shape(x)[0], tf.float32)
_, final_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, x, initial_state_fw=init_state_fw, initial_state_bw=init_state_bw)
res = tf.layers.dense(tf.concat([final_state[0][-1],final_state[1][-1]], 1), dim_y)
return res
def _rnnencoder(self, x, st, dim_h, dim_y, reuse=False):
with tf.variable_scope('rnnencoder', reuse=reuse):
cell_fw = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(dim_h) for _ in range(self.n_hidden)])
cell_bw = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(dim_h) for _ in range(self.n_hidden)])
init_state_fw = tf.layers.dense(st, dim_h, activation = tf.nn.sigmoid)
init_state_bw = tf.layers.dense(st, dim_h, activation = tf.nn.sigmoid)
peek_in = tf.layers.dense(st, self.dim_x, activation = tf.nn.sigmoid)
peek = tf.reshape(tf.tile(peek_in, [1, self.seqlen_x]), [-1, self.seqlen_x, self.dim_x])
_, final_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, tf.concat([x,peek],2),
initial_state_fw=tuple([init_state_fw]*self.n_hidden), initial_state_bw=tuple([init_state_bw]*self.n_hidden))
res = tf.layers.dense(tf.concat([final_state[0][-1],final_state[1][-1]], 1), dim_y)
return res
def _rnndecoder(self, x, st, dim_h, dim_y, reuse=False):
with tf.variable_scope('rnndecoder', reuse=reuse):
cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(dim_h) for _ in range(self.n_hidden)])
init_state = tf.layers.dense(st, dim_h, activation = tf.nn.sigmoid)
peek_in = tf.layers.dense(st, self.dim_x, activation = tf.nn.sigmoid)
peek = tf.reshape(tf.tile(peek_in, [1, self.seqlen_x]), [-1, self.seqlen_x, self.dim_x])
rnn_outputs, final_state = tf.nn.dynamic_rnn(cell, tf.concat([x,peek],2), initial_state=tuple([init_state]*self.n_hidden))
res = tf.layers.dense(rnn_outputs, dim_y)
return res