def main():
# Generate synthetic data
x = 2 * npr.rand(N, D) - 1 # data features, an (N,D) array
x[:, 0] = 1
th_true = 10.0 * np.array([0, 1, 1])
y = np.dot(x, th_true[:, None])[:, 0]
t = npr.rand(N) > (1 / (1 + np.exp(y))
) # data targets, an (N) array of 0s and 1s
# Obtain joint distributions over z and th
model = ff.LogisticModel(x, t, th0=th0, y0=y0)
# Set up step functions
th = np.random.randn(D) * th0
z = ff.BrightnessVars(N)
th_stepper = ff.ThetaStepMH(model.log_p_joint, stepsize)
z__stepper = ff.zStepMH(model.log_pseudo_lik, q)
plt.ion()
ax = plt.figure(figsize=(8, 6)).add_subplot(111)
while True:
th = th_stepper.step(th, z) # Markov transition step for theta
z = z__stepper.step(th, z) # Markov transition step for z
update_fig(ax, x, y, z, th, t)
plt.draw()
plt.pause(0.05)
def main():
global N
global D
x, t = data.load_logistic_data()
print x.shape, t.shape
N, D = x.shape
y0 = 1.5 # \xce in paper
performance_results = []
model_mcmc = ff.LogisticModel(x, t, th0=th0, y0=y0, name="regular mcmc")
mcmc_performance = run_model(model_mcmc)
performance_results.append(mcmc_performance)
model_untuned_flymc = ff.LogisticModel(x,
t,
th0=th0,
y0=y0,
name="untuned_flymc")
untuned_flymc_performance = run_model(model_untuned_flymc, q=0.1,
fly=True) # q = prob(dim -> bright)
performance_results.append(untuned_flymc_performance)
tmp_model = ff.LogisticModel(x, t, th0=th0)
th_map = optimize.minimize(
fun=lambda th: -1.0 * tmp_model.log_p_marg(th),
x0=np.random.randn(D) * th0,
jac=lambda th: -1.0 * tmp_model.D_log_p_marg(th),
method='BFGS',
options={
'maxiter': 100,
'disp': True
})
model_tuned_flymc = ff.LogisticModel(x,
t,
th0=th0,
th_map=th_map.x,
name="tuned_flymc")
tuned_flymc_performance = run_model(model_tuned_flymc, q=0.01, fly=True)
performance_results.append(tuned_flymc_performance)
save_results(performance_results)
def logistic_regression_chain(x,
t,
N_iter=100,
stepsize=1,
th0=1,
q=0.1,
y0=1,
seed=None):
# Set seed
npr.seed(seed)
# Obtain joint distributions over z and th and set step functions
model = ff.LogisticModel(x, t, th0=th0, y0=y0)
z__stepper = ff.zStepMH(model.log_pseudo_lik, q)
th_stepper = ff.ThetaStepMH(model.log_p_joint, stepsize)
# Initialize
N, D = x.shape
th = np.random.randn(D) * th0
z = ff.BrightnessVars(N)
# run chain
th_chain = np.zeros((N_iter, ) + th.shape)
z_chain = np.zeros((N_iter, N), dtype=bool)
for i in range(N_iter):
th = th_stepper.step(th, z)
z = z__stepper.step(th, z)
# Record the intermediate results
th_chain[i, :] = th.copy()
z_chain[i, z.bright] = 1
print "th0 = ", th0, "frac accepted is", th_stepper.frac_accepted, "bright frac is:", np.mean(
z_chain)
return th_chain, z_chain
def main():
mndata = MNIST('.')
mndata.load_training()
#mndata.load_testing()
ss_idx = filter(lambda i: mndata.train_labels[i] in [7, 9],
range(len(mndata.train_images)))
data_ss = np.array(mndata.train_images)[ss_idx, :]
label_ss = np.array(mndata.train_labels)[ss_idx]
pca = PCA(n_components=10) # TODO: change to 50
pca.fit(data_ss)
x = pca.transform(data_ss)
x = np.concatenate((x, np.ones((x.shape[0], 1))), axis=1)
t = label_ss == 7
N, D = x.shape
y0 = 1.5 # \xce in paper
# Generate synthetic data
# x = 2 * npr.rand(N,D) - 1 # data features, an (N,D) array
# x[:, 0] = 1
# th_true = 10.0 * np.array([0, 1, 1])
# y = np.dot(x, th_true[:, None])[:, 0]
# t = npr.rand(N) > (1 / ( 1 + np.exp(y))) # data targets, an (N) array of 0s and 1s
# Obtain joint distributions over z and th
# Set up step functions
def run_model(model, th_init=np.random.randn(D) * th0, q=0.1, fly=False):
th = th_init
if fly:
z = ff.BrightnessVars(N)
else:
z = ff.BrightnessVars(N, range(N))
th_stepper = ff.ThetaStepMH(model.log_p_joint, stepsize)
if fly:
z__stepper = ff.zStepMH(model.log_pseudo_lik, q)
ths = []
num_rejects = 0
for i in range(N_steps):
num_lik_prev = model.num_lik_evals
if i % N_ess == 0 and i > 0:
#print pypmc.tools.convergence.ess(ths) # TODO: is this correct?
#print ess(ths)
np.savetxt('trace-untuned-{0}.csv'.format(i), np.array(ths))
ths = []
th = th_stepper.step(th, z) # Markov transition step for theta
num_rejects += th_stepper.num_rejects
if fly:
z = z__stepper.step(th, z) # Markov transition step for z
ths.append(th)
print "\t\t".join(
map(lambda x: "{0:.5f}".format(x), [
i,
len(z.bright), model.num_lik_evals - num_lik_prev,
1.0 - num_rejects / float(i + 1),
-1.0 * model.log_p_marg(th, increment_ctr=False)
]))
return th
def ess(th_list):
th = np.array(th_list)
th_mean = np.mean(th, axis=0)
def autocorr(x, t):
return np.mean(
(x[0:len(x) - t, :] - th_mean) * (x[t:len(x), :] - th_mean))
return 1.0 * th.shape[0] / (
1.0 +
2.0 * sum(map(lambda t: autocorr(th, t), range(1, th.shape[0]))))
# print ess([
# np.array([1]),
# np.array([1.1]),
# np.array([0.9]),
# np.array([1])
# ])
print "Running MCMC"
#model_mcmc = ff.LogisticModel(x, t, th0=th0, y0=y0)
#print run_model(model_mcmc)
print "Running untuned FlyMC"
#model_flymc = ff.LogisticModel(x, t, th0=th0, y0=y0)
#print run_model(model_flymc, q=0.1, fly=True) # q = prob(dim -> bright)
print "Running MAP-tuned FlyMC"
_model = ff.LogisticModel(x, t, th0=th0)
th_map = optimize.minimize(fun=lambda th: -1.0 * _model.log_p_marg(th),
x0=np.random.randn(D) * th0,
jac=lambda th: -1.0 * _model.D_log_p_marg(th),
method='BFGS',
options={
'maxiter': 100,
'disp': True
})
model_flymc_map = ff.LogisticModel(x, t, th0=th0, th_map=th_map.x)
print run_model(
model_flymc_map,
#th_init=th_map.x, # TODO: is it okay to initialize at the MAP?
q=0.01,
fly=True)
def random_model(self, th_map=None):
x = npr.randn(self.N, self.D)
t = npr.randint(2, size=self.N)
return ff.LogisticModel(x, t, th_map=th_map)
def create_toy_logistic_model():
x = np.array([[2.2, 1.7], [-1.5, 2.9], [1.4, -1.2]])
t = np.array([1, 0, 1])
th0 = 1.2
y0 = 2
return ff.LogisticModel(x, t, th0=th0, y0=y0)
def main():
def run_model(model, q=0.1, fly=False):
'''
function to run model
'''
th = np.random.randn(D) * th0
if fly:
z = ff.BrightnessVars(N, range(int(q * N)))
else:
z = ff.BrightnessVars(N, range(N))
th_stepper = ff.ThetaStepMH(model.log_p_joint, stepsize)
if fly:
z__stepper = ff.zStepMH(model.log_pseudo_lik, q)
th_lists = [] # trace of th
num_rejects_list = [] #
num_iter_list = [] # number of num_lik_evals for each iteration
for _ in range(N_steps):
num_lik_prev = model.num_lik_evals
if _ % N_ess == 0 and _ > 0:
#print pypmc.tools.convergence.ess(th_lists) # TODO: is this correct?
#print ess(th_lists)
np.savetxt('trace-untuned-{0}.csv'.format(_),
np.array(th_lists))
th_lists = []
th = th_stepper.step(th, z) # Markov transition step for theta
if fly:
z = z__stepper.step(th, z) # Markov transition step for z
th_lists.append(th)
num_rejects_list.append(th_stepper.num_rejects)
num_iter_list.append(model.num_lik_evals - num_lik_prev)
print "Accept rate: {0}".format(1.0 - sum(num_rejects_list) /
float(_ + 1))
print "Likelihood evals in iter {0}: {1}".format(
_, model.num_lik_evals - num_lik_prev)
print "Neg log posterior: {0}".format(
-1.0 * model.log_p_marg(th, increment_ctr=False))
print "Number bright points: {0}".format(len(z.bright))
return th, num_iter_list, th_lists
# preprocess data and save to .npy file
# so tha we dont have to PCA them each time
# preprocess()
x = np.load('logistic_x.npy')
t = np.load('logistic_t.npy')
print x.shape, t.shape
N, D = x.shape
y0 = 1.5 # \xce in paper
def ess(th_list):
th = np.array(th_list)
th_mean = np.mean(th, axis=0)
def autocorr(x, t):
return np.mean(
(x[0:len(x) - t, :] - th_mean) * (x[t:len(x), :] - th_mean))
return 1.0 * th.shape[0] / (
1.0 +
2.0 * sum(map(lambda t: autocorr(th, t), range(1, th.shape[0]))))
# print ess([
# np.array([1]),
# np.array([1.1]),
# np.array([0.9]),
# np.array([1])
# ])
# model_mcmc = ff.LogisticModel(x, t, th0=th0, y0=y0)
# print model_mcmc.num_lik_evals
# th_mcmc, num_iter_list_mcmc, th_lists_mcmc = run_model(model_mcmc)
# print model_mcmc.num_lik_evals
model_flymc = ff.LogisticModel(x, t, th0=th0, y0=y0)
# print model_flymc.num_lik_evals
th_flymc, num_iter_list_flymc, th_lists_flymc = run_model(
model_flymc, q=0.1, fly=True) # q = prob(dim -> bright)
# print model_flymc.num_lik_evals
#_model = ff.LogisticModel(x, t, th0=th0)
#th = np.random.randn(D) * th0
#th_map = optimize.minimize(lambda x: -1*_model.log_p_marg(x), th)
#model_flymc_map = ff.LogisticModel(x, t, th0=th0, th_map=th_map.x)
#print run_model(model_flymc_map, q=0.01, fly=True)
#print model_flymc_map.num_lik_evals
# output traces to .csv
# print num_iter_list_flymc
# with open('num_iter_list_mcmc', 'wb') as f:
# writer = csv.writer(f)
# writer.writerows(num_iter_list_flymc)
# plt.plot(num_iter_list_mcmc)
plt.plot(num_iter_list_flymc)
plt.show()