This repository has been archived by the owner on Jul 17, 2020. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 100
/
cevae_ihdp.py
222 lines (179 loc) · 11 KB
/
cevae_ihdp.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
#!/usr/bin/env python
"""CEVAE model on IHDP
"""
import edward as ed
import tensorflow as tf
from edward.models import Bernoulli, Normal
from progressbar import ETA, Bar, Percentage, ProgressBar
from datasets import IHDP
from evaluation import Evaluator
import numpy as np
import time
from scipy.stats import sem
from utils import fc_net, get_y0_y1
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('-reps', type=int, default=10)
parser.add_argument('-earl', type=int, default=10)
parser.add_argument('-lr', type=float, default=0.001)
parser.add_argument('-opt', choices=['adam', 'adamax'], default='adam')
parser.add_argument('-epochs', type=int, default=100)
parser.add_argument('-print_every', type=int, default=10)
args = parser.parse_args()
args.true_post = True
dataset = IHDP(replications=args.reps)
dimx = 25
scores = np.zeros((args.reps, 3))
scores_test = np.zeros((args.reps, 3))
M = None # batch size during training
d = 20 # latent dimension
lamba = 1e-4 # weight decay
nh, h = 3, 200 # number and size of hidden layers
for i, (train, valid, test, contfeats, binfeats) in enumerate(dataset.get_train_valid_test()):
print('\nReplication {}/{}'.format(i + 1, args.reps))
(xtr, ttr, ytr), (y_cftr, mu0tr, mu1tr) = train
(xva, tva, yva), (y_cfva, mu0va, mu1va) = valid
(xte, tte, yte), (y_cfte, mu0te, mu1te) = test
evaluator_test = Evaluator(yte, tte, y_cf=y_cfte, mu0=mu0te, mu1=mu1te)
# reorder features with binary first and continuous after
perm = binfeats + contfeats
xtr, xva, xte = xtr[:, perm], xva[:, perm], xte[:, perm]
xalltr, talltr, yalltr = np.concatenate([xtr, xva], axis=0), np.concatenate([ttr, tva], axis=0), np.concatenate([ytr, yva], axis=0)
evaluator_train = Evaluator(yalltr, talltr, y_cf=np.concatenate([y_cftr, y_cfva], axis=0),
mu0=np.concatenate([mu0tr, mu0va], axis=0), mu1=np.concatenate([mu1tr, mu1va], axis=0))
# zero mean, unit variance for y during training
ym, ys = np.mean(ytr), np.std(ytr)
ytr, yva = (ytr - ym) / ys, (yva - ym) / ys
best_logpvalid = - np.inf
with tf.Graph().as_default():
sess = tf.InteractiveSession()
ed.set_seed(1)
np.random.seed(1)
tf.set_random_seed(1)
x_ph_bin = tf.placeholder(tf.float32, [M, len(binfeats)], name='x_bin') # binary inputs
x_ph_cont = tf.placeholder(tf.float32, [M, len(contfeats)], name='x_cont') # continuous inputs
t_ph = tf.placeholder(tf.float32, [M, 1])
y_ph = tf.placeholder(tf.float32, [M, 1])
x_ph = tf.concat([x_ph_bin, x_ph_cont], 1)
activation = tf.nn.elu
# CEVAE model (decoder)
# p(z)
z = Normal(loc=tf.zeros([tf.shape(x_ph)[0], d]), scale=tf.ones([tf.shape(x_ph)[0], d]))
# p(x|z)
hx = fc_net(z, (nh - 1) * [h], [], 'px_z_shared', lamba=lamba, activation=activation)
logits = fc_net(hx, [h], [[len(binfeats), None]], 'px_z_bin'.format(i + 1), lamba=lamba, activation=activation)
x1 = Bernoulli(logits=logits, dtype=tf.float32, name='bernoulli_px_z')
mu, sigma = fc_net(hx, [h], [[len(contfeats), None], [len(contfeats), tf.nn.softplus]], 'px_z_cont', lamba=lamba,
activation=activation)
x2 = Normal(loc=mu, scale=sigma, name='gaussian_px_z')
# p(t|z)
logits = fc_net(z, [h], [[1, None]], 'pt_z', lamba=lamba, activation=activation)
t = Bernoulli(logits=logits, dtype=tf.float32)
# p(y|t,z)
mu2_t0 = fc_net(z, nh * [h], [[1, None]], 'py_t0z', lamba=lamba, activation=activation)
mu2_t1 = fc_net(z, nh * [h], [[1, None]], 'py_t1z', lamba=lamba, activation=activation)
y = Normal(loc=t * mu2_t1 + (1. - t) * mu2_t0, scale=tf.ones_like(mu2_t0))
# CEVAE variational approximation (encoder)
# q(t|x)
logits_t = fc_net(x_ph, [d], [[1, None]], 'qt', lamba=lamba, activation=activation)
qt = Bernoulli(logits=logits_t, dtype=tf.float32)
# q(y|x,t)
hqy = fc_net(x_ph, (nh - 1) * [h], [], 'qy_xt_shared', lamba=lamba, activation=activation)
mu_qy_t0 = fc_net(hqy, [h], [[1, None]], 'qy_xt0', lamba=lamba, activation=activation)
mu_qy_t1 = fc_net(hqy, [h], [[1, None]], 'qy_xt1', lamba=lamba, activation=activation)
qy = Normal(loc=qt * mu_qy_t1 + (1. - qt) * mu_qy_t0, scale=tf.ones_like(mu_qy_t0))
# q(z|x,t,y)
inpt2 = tf.concat([x_ph, qy], 1)
hqz = fc_net(inpt2, (nh - 1) * [h], [], 'qz_xty_shared', lamba=lamba, activation=activation)
muq_t0, sigmaq_t0 = fc_net(hqz, [h], [[d, None], [d, tf.nn.softplus]], 'qz_xt0', lamba=lamba,
activation=activation)
muq_t1, sigmaq_t1 = fc_net(hqz, [h], [[d, None], [d, tf.nn.softplus]], 'qz_xt1', lamba=lamba,
activation=activation)
qz = Normal(loc=qt * muq_t1 + (1. - qt) * muq_t0, scale=qt * sigmaq_t1 + (1. - qt) * sigmaq_t0)
# Create data dictionary for edward
data = {x1: x_ph_bin, x2: x_ph_cont, y: y_ph, qt: t_ph, t: t_ph, qy: y_ph}
# sample posterior predictive for p(y|z,t)
y_post = ed.copy(y, {z: qz, t: t_ph}, scope='y_post')
# crude approximation of the above
y_post_mean = ed.copy(y, {z: qz.mean(), t: t_ph}, scope='y_post_mean')
# construct a deterministic version (i.e. use the mean of the approximate posterior) of the lower bound
# for early stopping according to a validation set
y_post_eval = ed.copy(y, {z: qz.mean(), qt: t_ph, qy: y_ph, t: t_ph}, scope='y_post_eval')
x1_post_eval = ed.copy(x1, {z: qz.mean(), qt: t_ph, qy: y_ph}, scope='x1_post_eval')
x2_post_eval = ed.copy(x2, {z: qz.mean(), qt: t_ph, qy: y_ph}, scope='x2_post_eval')
t_post_eval = ed.copy(t, {z: qz.mean(), qt: t_ph, qy: y_ph}, scope='t_post_eval')
logp_valid = tf.reduce_mean(tf.reduce_sum(y_post_eval.log_prob(y_ph) + t_post_eval.log_prob(t_ph), axis=1) +
tf.reduce_sum(x1_post_eval.log_prob(x_ph_bin), axis=1) +
tf.reduce_sum(x2_post_eval.log_prob(x_ph_cont), axis=1) +
tf.reduce_sum(z.log_prob(qz.mean()) - qz.log_prob(qz.mean()), axis=1))
inference = ed.KLqp({z: qz}, data)
optimizer = tf.train.AdamOptimizer(learning_rate=args.lr)
inference.initialize(optimizer=optimizer)
saver = tf.train.Saver(tf.contrib.slim.get_variables())
tf.global_variables_initializer().run()
n_epoch, n_iter_per_epoch, idx = args.epochs, 10 * int(xtr.shape[0] / 100), np.arange(xtr.shape[0])
# dictionaries needed for evaluation
tr0, tr1 = np.zeros((xalltr.shape[0], 1)), np.ones((xalltr.shape[0], 1))
tr0t, tr1t = np.zeros((xte.shape[0], 1)), np.ones((xte.shape[0], 1))
f1 = {x_ph_bin: xalltr[:, 0:len(binfeats)], x_ph_cont: xalltr[:, len(binfeats):], t_ph: tr1}
f0 = {x_ph_bin: xalltr[:, 0:len(binfeats)], x_ph_cont: xalltr[:, len(binfeats):], t_ph: tr0}
f1t = {x_ph_bin: xte[:, 0:len(binfeats)], x_ph_cont: xte[:, len(binfeats):], t_ph: tr1t}
f0t = {x_ph_bin: xte[:, 0:len(binfeats)], x_ph_cont: xte[:, len(binfeats):], t_ph: tr0t}
for epoch in range(n_epoch):
avg_loss = 0.0
t0 = time.time()
widgets = ["epoch #%d|" % epoch, Percentage(), Bar(), ETA()]
pbar = ProgressBar(n_iter_per_epoch, widgets=widgets)
pbar.start()
np.random.shuffle(idx)
for j in range(n_iter_per_epoch):
pbar.update(j)
batch = np.random.choice(idx, 100)
x_train, y_train, t_train = xtr[batch], ytr[batch], ttr[batch]
info_dict = inference.update(feed_dict={x_ph_bin: x_train[:, 0:len(binfeats)],
x_ph_cont: x_train[:, len(binfeats):],
t_ph: t_train, y_ph: y_train})
avg_loss += info_dict['loss']
avg_loss = avg_loss / n_iter_per_epoch
avg_loss = avg_loss / 100
if epoch % args.earl == 0 or epoch == (n_epoch - 1):
logpvalid = sess.run(logp_valid, feed_dict={x_ph_bin: xva[:, 0:len(binfeats)], x_ph_cont: xva[:, len(binfeats):],
t_ph: tva, y_ph: yva})
if logpvalid >= best_logpvalid:
print('Improved validation bound, old: {:0.3f}, new: {:0.3f}'.format(best_logpvalid, logpvalid))
best_logpvalid = logpvalid
saver.save(sess, 'models/m6-ihdp')
if epoch % args.print_every == 0:
y0, y1 = get_y0_y1(sess, y_post, f0, f1, shape=yalltr.shape, L=1)
y0, y1 = y0 * ys + ym, y1 * ys + ym
score_train = evaluator_train.calc_stats(y1, y0)
rmses_train = evaluator_train.y_errors(y0, y1)
y0, y1 = get_y0_y1(sess, y_post, f0t, f1t, shape=yte.shape, L=1)
y0, y1 = y0 * ys + ym, y1 * ys + ym
score_test = evaluator_test.calc_stats(y1, y0)
print("Epoch: {}/{}, log p(x) >= {:0.3f}, ite_tr: {:0.3f}, ate_tr: {:0.3f}, pehe_tr: {:0.3f}, " \
"rmse_f_tr: {:0.3f}, rmse_cf_tr: {:0.3f}, ite_te: {:0.3f}, ate_te: {:0.3f}, pehe_te: {:0.3f}, " \
"dt: {:0.3f}".format(epoch + 1, n_epoch, avg_loss, score_train[0], score_train[1], score_train[2],
rmses_train[0], rmses_train[1], score_test[0], score_test[1], score_test[2],
time.time() - t0))
saver.restore(sess, 'models/m6-ihdp')
y0, y1 = get_y0_y1(sess, y_post, f0, f1, shape=yalltr.shape, L=100)
y0, y1 = y0 * ys + ym, y1 * ys + ym
score = evaluator_train.calc_stats(y1, y0)
scores[i, :] = score
y0t, y1t = get_y0_y1(sess, y_post, f0t, f1t, shape=yte.shape, L=100)
y0t, y1t = y0t * ys + ym, y1t * ys + ym
score_test = evaluator_test.calc_stats(y1t, y0t)
scores_test[i, :] = score_test
print('Replication: {}/{}, tr_ite: {:0.3f}, tr_ate: {:0.3f}, tr_pehe: {:0.3f}' \
', te_ite: {:0.3f}, te_ate: {:0.3f}, te_pehe: {:0.3f}'.format(i + 1, args.reps,
score[0], score[1], score[2],
score_test[0], score_test[1], score_test[2]))
sess.close()
print('CEVAE model total scores')
means, stds = np.mean(scores, axis=0), sem(scores, axis=0)
print('train ITE: {:.3f}+-{:.3f}, train ATE: {:.3f}+-{:.3f}, train PEHE: {:.3f}+-{:.3f}' \
''.format(means[0], stds[0], means[1], stds[1], means[2], stds[2]))
means, stds = np.mean(scores_test, axis=0), sem(scores_test, axis=0)
print('test ITE: {:.3f}+-{:.3f}, test ATE: {:.3f}+-{:.3f}, test PEHE: {:.3f}+-{:.3f}' \
''.format(means[0], stds[0], means[1], stds[1], means[2], stds[2]))