def cross_mi_3D(data, settings):
nTrial, nProcess, nSample = data.shape
if nTrial*nSample < 2 * nProcess:
# If there are too few samples, there is no point to calculate anything
return np.full((nProcess, nProcess), np.nan)
else:
lag = settings['lag']
# Check that number of timesteps is sufficient to estimate lagMax
if nSample <= lag:
raise ValueError('lag', lag, 'cannot be estimated for number of timesteps', nSample)
# dataCanon = numpy_transpose_byorder(data, 'rps', 'srp')
dataOrd = numpy_transpose_byorder(data, 'rps', 'psr')
xx = numpy_merge_dimensions(dataOrd[:, :nSample-lag], 1, 3)
yy = numpy_merge_dimensions(dataOrd[:, lag:], 1, 3)
rez = np.zeros((nProcess, nProcess))
if lag > 0:
for i in range(nProcess):
for j in range(nProcess):
rez[i][j] = ee.mi(xx[i], yy[j])
else:
# Optimization - take advantage of symmetry
for i in range(nProcess):
for j in range(i, nProcess):
rez[i][j] = ee.mi(xx[i], yy[j])
rez[j][i] = rez[i][j]
return rez
def evaluateModel(self):
with tf.Session() as sess:
# Restore variables from disk.
self.saver.restore(sess, self.args.pretrained)
x, y, _, bins = self.dataset.get_eval_data()
mu_out, x_rec = sess.run([self.mu, self.x_mu],
feed_dict={
self.tf_X: x,
self.tf_Y: y,
self.lagMul: np.asarray([[self.lm]])
})
x_mae_out, z0 = sess.run([self.x_mae, self.z_plain],
feed_dict={
self.tf_X: x,
self.tf_Y: y,
self.z_plain: mu_out
})
mi = ee.mi(z0, y, k=20)
self.log.info("Mutual Information: %f, X MAE: %f" %
(mi, x_mae_out))
tmp_idx = np.random.choice(10000, 1000)
def cmi(self, X, Y, Z):
np.random.seed(0)
r = ee.mi(X.copy(order='C'),
Y.copy(order='C'),
z=Z.copy(order='C'),
k=self.k)
return r if r >= 0 else 0
def average_predictive_info(data, settings):
x, y = drop_nan_rows(split3D(data, settings['max_lag']))
nSample, nProcess = x.shape
if nSample < 5 + 5 * nProcess:
# If there are too few samples, there is no point to calculate anything
return np.array(np.nan)
else:
return ee.mi(x, y) / nProcess
def responsiveness_1(keyp, keyp_diff, actions):
assert len(keyp.shape) == 2 and keyp.shape[1] == 2
keyp_combine = np.hstack((actions, keyp))
print(keyp_combine.shape)
resp = ee.mi(keyp_combine, keyp_diff)
return resp
def do_it(X,Y,X_corrected,Y_corrected,do_entropy=True):
#pearsons
corr = np.corrcoef(X,Y)[0,1]
varx = np.var(X)
vary = np.var(Y)
cov = np.cov(X,Y)[0,1]
meanx = np.mean(X)
meany = np.mean(Y)
#concordance
conc = 2.*float(cov)/(float(varx)+float(vary)+np.square(float(meanx)-float(meany)))
#spearman
spearman=spearmanr(X,Y)[0]
#dice coefficient
#by percentile:
# dice = pandas.Series()
#U = np.union1d(X,Y)
# for percent in [100,50,25,10,5,1]:
# threshold = np.percentile(abs(U),percent)
# x = abs(X)>threshold
# y = abs(Y)>threshold
# s = x*y
for threshold in [0]:
x_pos = X_corrected>threshold
x_neg = X_corrected<threshold
y_pos = Y_corrected>threshold
y_neg = Y_corrected<threshold
pos = x_pos*y_pos
neg = x_neg*y_neg
s = pos+neg
xplusy = x_pos.sum()+x_neg.sum()+y_pos.sum()+y_neg.sum()
if xplusy == 0:
die = 0
else:
die = 2.*s.sum()/xplusy
# dice[str(percent)+'_'+str(threshold)] = die
dice = die
if do_entropy:
#entropy
x = ee.vectorize(X)
y = ee.vectorize(Y)
mi = ee.mi(x,y)
hx = -ee.entropy(x)
hy = -ee.entropy(y)
ecc= np.sqrt(mi/(0.5*(hx+hy)))
else:
ecc = np.nan
return corr, conc, spearman, dice, ecc
def responsiveness(keyp, actions):
"""
:param keyp: T x 2
:param actions: T x 1
:return:
"""
assert len(keyp.shape) == 2 and keyp.shape[1] == 2
resp = ee.mi(keyp, actions)
return resp
def mi(self, X, Y):
dx, dy, dz = X.shape[-1], Y.shape[-1], 0
d = dx + dy + dz
k = np.clip(2 * d, 7, 20)
if (k, dx, dy, dz) in self.alphas:
alpha = self.alphas[k, dx, dy, dz]
else:
alpha = None
np.random.seed(0)
r = ee.mi(X.copy(order='C'), Y.copy(order='C'), k=k, alpha=alpha)
return r if r >= 0 else 0
def ksg(x, y):
"""
Kraskov–Stogbauer–Grassberger (KSG) estimator of mutual information
between two sentences represented as word embedding matrices x and y
:param x: list of word embeddings for the first sentence
:param y: list of word embeddings for the second sentence
:return: KSG similarity measure between the two sentences
"""
if pool is None:
xT = x.T
yT = y.T
else:
xT = pool(x, axis=0).reshape(-1, 1)
yT = pool(y, axis=0).reshape(-1, 1)
return mi(xT, yT, base=np.e, k=k)
def evaluateModel(self):
with tf.Session() as sess:
# Restore variables from disk.
self.saver.restore(sess, self.args.pretrained)
x,y,_,bins = self.dataset.get_eval_data()
mu_out, x_rec, y_rec = sess.run(
[self.mu, self.x_mu, self.y_mu],
feed_dict={self.tf_X: x, self.tf_Y: y, self.lagMul: np.asarray([[self.lm]])})
x_mae_out, y_o, z1, z0 = sess.run([self.x_mae, self.y_mae, self.z_1, self.z_0],
feed_dict={self.tf_X: x, self.tf_Y: y, self.lagMul: np.asarray([[self.lm]]), self.z: mu_out})
mi = ee.mi(z0, y, k=20)
self.log.info("Mutual Information: %f, X MAE: %f, Y MAE: %f" % (mi, x_mae_out, y_o))
Plotter.plot2DData(y_rec[:, :], bins[:], self.args.plot_path, Strings.STIB_WO_REG_Y)
Plotter.plot2DLatentSpace(z0[:, 0], z1[:], bins[:], self.args.plot_path, Strings.STIB_WO_REG_LATENT)
Plotter.plot2DData(x_rec[:, :], bins[:], self.args.plot_path, Strings.STIB_WO_REG_X)
def average_predictive_info_non_uniform(dataLst, settings):
# Test that all trials have sufficient timesteps for lag estimation
nSampleMin = np.min(set_list_shapes(dataLst, axis=1))
if nSampleMin <= settings['max_lag']:
raise ValueError('lag', settings['max_lag'], 'cannot be estimated for number of timesteps', nSampleMin)
xLst = []
yLst = []
for dataTrial in dataLst:
x, y = drop_nan_rows(split3D(dataTrial, settings['max_lag']))
xLst += [x]
yLst += [y]
xArr = np.vstack(xLst)
yArr = np.vstack(yLst)
nSample, nProcess = xArr.shape
if nSample < 4 * nProcess:
# If there are too few samples, there is no point to calculate anything
return np.array(np.nan)
else:
return ee.mi(xArr, yArr) / nProcess
print('Mutual Information')
trueent = 0.5 * (1 + log(2. * pi * cov[0][0])) # x sub
trueent += 0.5 * (1 + log(2. * pi * cov[1][1])) # y sub
trueent += -0.5 * (2 + log(4. * pi * pi * det([[cov[0][0], cov[0][1]], [cov[1][0], cov[1][1]]]))) # xz sub
print('true MI(x:y)', trueent / log(2))
ent = []
err = []
for NN in Ntry:
tempent = []
for j in range(nsamples):
points = nr.multivariate_normal(mean, cov, NN)
x = [point[:1] for point in points]
y = [point[1:2] for point in points]
tempent.append(ee.mi(x, y))
tempent.sort()
tempmean = np.mean(tempent)
ent.append(tempmean)
err.append((tempmean - tempent[samplo], tempent[samphi] - tempmean))
print('samples used', Ntry)
print('estimated MI', ent)
print('95% conf int.\n', err)
print('\nIF you permute the indices of x, e.g., MI(X:Y) = 0')
# You can use shuffle_test method to just get mean, standard deviation
ent = []
err = []
for NN in Ntry:
print('Mutual Information')
trueent = 0.5 * (1 + log(2. * pi * cov[0][0])) # x sub
trueent += 0.5 * (1 + log(2. * pi * cov[1][1])) # y sub
trueent += -0.5 * (2 + log(4. * pi * pi * det(
[[cov[0][0], cov[0][1]], [cov[1][0], cov[1][1]]]))) # xz sub
print('true MI(x:y)', trueent / log(2))
ent = []
err = []
for NN in Ntry:
tempent = []
for j in range(nsamples):
points = nr.multivariate_normal(mean, cov, NN)
x = [point[:1] for point in points]
y = [point[1:2] for point in points]
tempent.append(ee.mi(x, y))
tempent.sort()
tempmean = np.mean(tempent)
ent.append(tempmean)
err.append((tempmean - tempent[samplo], tempent[samphi] - tempmean))
print('samples used', Ntry)
print('estimated MI', ent)
print('95% conf int.\n', err)
print('\nIF you permute the indices of x, e.g., MI(X:Y) = 0')
# You can use shuffle_test method to just get mean, standard deviation
ent = []
err = []
for NN in Ntry:
tempent = []
def optimizeModel(self):
lm = 0.7
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
saver = tf.train.Saver(max_to_keep=1000000)
x_l_t = np.inf
y_l_t = np.inf
x, y, _, bins = self.dataset.get_eval_data()
with tf.Session(config=config) as sess:
sess.run(tf.initialize_all_variables())
for iter in range(150000):
x_batch, y_batch, t_batch = self.dataset.next_batch(
self.batch_size)
_, mu_out, ll_out, rl_out, log_sigma_out, yl_out, irl_out, z0_out_tr = sess.run(
[
self.optimizer, self.mu, self.latent_loss,
self.x_reconstr_loss, self.log_sigma,
self.y_reconstr_loss, self.ir_loss, self.z_0
],
feed_dict={
self.tf_X: x_batch,
self.tf_Y: y_batch,
self.lagMul: np.asarray([[lm]])
})
_, irl_out_adv, z_out, y_mur_sg_out, bi_loss_out = sess.run(
[
self.optimizer_ir, self.ir_loss, self.z, self.y_mur_sg,
self.bi_loss
],
feed_dict={
self.tf_X: x_batch,
self.tf_Y: y_batch,
self.lagMul: np.asarray([[lm]])
})
if ((iter) % 300 == 0) and iter > 1:
self.log.info(
"Iteration: %d, Lambda: %.2f, I(x,t): %.2f, I(Z,X): %.2f, I(Z1,Y): %.2f"
% (iter, lm, ll_out, rl_out, yl_out))
if iter % 300 == 0 and iter > 0:
mu_out_tmp = sess.run(
[self.mu],
feed_dict={
self.tf_X: x,
self.tf_Y: y,
self.lagMul: np.asarray([[lm]])
})
x_rec, y_rec, y_irr_rec, ll_test, y_rec_test_loss, y_irr_test_loss, mi_loss, y_o, y_ir_o, y_hat_test, z0_out, x_test_loss, x_mae_out, bi_out = sess.run(
[
self.x_mu, self.y_mu, self.y_mur_sg,
self.latent_loss, self.y_reconstr_loss,
self.y_irr_rec_loss, self.ir_loss, self.y_mae,
self.y_irr_mae, self.y_hat, self.z_0,
self.x_reconstr_loss, self.x_mae, self.bi_loss
],
feed_dict={
self.tf_X: x,
self.tf_Y: y,
self.lagMul: np.asarray([[lm]]),
self.z: np.reshape(mu_out_tmp, (10000, 3))
})
self.log.info(
"I(Z0,Y): %.2f, BI Loss: %.2f, X MAE: %.2f, Y MAE: %.2f"
% (ee.mi(z0_out, y, k=20), bi_out, x_mae_out, y_o))
if y_o <= y_l_t and x_mae_out <= x_l_t:
y_l_t = y_o
x_l_t = x_mae_out
self.log.info("saved model ...")
save_path = saver.save(sess, self.args.save_path)
lm = lm * 1.03