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TestImpTM.py
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TestImpTM.py
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##################################################################
# Code for testing the variational Multi-Stage Generative Model. #
##################################################################
# basic python
import numpy as np
import numpy.random as npr
import cPickle
# theano business
import theano
import theano.tensor as T
# phil's sweetness
import utils
from GPSImputer import TemplateMatchImputer
from load_data import load_udm, load_mnist, load_tfd, load_svhn_gray
from HelperFuncs import construct_masked_data, shift_and_scale_into_01, \
row_shuffle, to_fX
###############################
###############################
## TEST GPS IMPUTER ON MNIST ##
###############################
###############################
def test_mnist_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_MNIST_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = datasets[0][0]
Xva = datasets[1][0]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1],)))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
out_file.close()
return
def test_mnist_img(occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = datasets[0][0]
Xva = datasets[1][0]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1],)))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[:500], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
img_match_on_known, img_match_on_unknown = TM.best_match_img(xo, xm)
display_count = 100
# visualize matches on known elements
Xs = np.zeros((2*display_count, Xva.shape[1]))
for idx in range(display_count):
Xs[2*idx] = xi[idx]
Xs[(2*idx)+1] = img_match_on_known[idx]
file_name = "{0:s}_SAMPLES_MOK.png".format(result_tag)
utils.visualize_samples(Xs, file_name, num_rows=20)
# visualize matches on unknown elements
Xs = np.zeros((2*display_count, Xva.shape[1]))
for idx in range(display_count):
Xs[2*idx] = xi[idx]
Xs[(2*idx)+1] = img_match_on_unknown[idx]
file_name = "{0:s}_SAMPLES_MOU.png".format(result_tag)
utils.visualize_samples(Xs, file_name, num_rows=20)
return
def test_tfd_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_TFD_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
data_file = 'data/tfd_data_48x48.pkl'
dataset = load_tfd(tfd_pkl_name=data_file, which_set='unlabeled', fold='all')
Xtr_unlabeled = dataset[0]
dataset = load_tfd(tfd_pkl_name=data_file, which_set='train', fold='all')
Xtr_train = dataset[0]
Xtr = np.vstack([Xtr_unlabeled, Xtr_train])
dataset = load_tfd(tfd_pkl_name=data_file, which_set='valid', fold='all')
Xva = dataset[0]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
out_file.close()
return
def test_svhn_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_SVHN_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
tr_file = 'data/svhn_train_gray.pkl'
te_file = 'data/svhn_test_gray.pkl'
ex_file = 'data/svhn_extra_gray.pkl'
data = load_svhn_gray(tr_file, te_file, ex_file=ex_file, ex_count=200000)
Xtr = to_fX( shift_and_scale_into_01(np.vstack([data['Xtr'], data['Xex']])) )
Xva = to_fX( shift_and_scale_into_01(data['Xte']) )
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
out_file.close()
return
if __name__=="__main__":
#########
# MNIST #
#########
# test_mnist_nll(occ_dim=0, drop_prob=0.6)
# test_mnist_nll(occ_dim=0, drop_prob=0.7)
# test_mnist_nll(occ_dim=0, drop_prob=0.8)
# test_mnist_nll(occ_dim=0, drop_prob=0.9)
# test_mnist_nll(occ_dim=14, drop_prob=0.0)
# test_mnist_nll(occ_dim=16, drop_prob=0.0)
# test_mnist_img(occ_dim=0, drop_prob=0.6)
# test_mnist_img(occ_dim=0, drop_prob=0.7)
# test_mnist_img(occ_dim=0, drop_prob=0.8)
# test_mnist_img(occ_dim=0, drop_prob=0.9)
# test_mnist_img(occ_dim=14, drop_prob=0.0)
# test_mnist_img(occ_dim=16, drop_prob=0.0)
########
# SVHN #
########
#test_svhn_nll(occ_dim=17, drop_prob=0.0)
#test_svhn_nll(occ_dim=0, drop_prob=0.8)
#######
# TFD #
#######
#test_tfd_nll(occ_dim=25, drop_prob=0.0)
test_tfd_nll(occ_dim=0, drop_prob=0.8)