def problem5(T = 10, L = 0.2):
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
plot_toy_results('Perceptron', thetas_perceptron, toy_features, toy_labels)
plot_toy_results('Average Perceptron', thetas_avg_perceptron, toy_features, toy_labels)
plot_toy_results('Pegasos', thetas_pegasos, toy_features, toy_labels)
def test_toy_data():
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
from sklearn import svm
clf = svm.SVR(C=1.0,
cache_size=200,
coef0=0.0,
degree=3,
epsilon=0.1,
gamma="auto",
kernel='rbf',
max_iter=-1,
shrinking=True,
tol=0.001,
verbose=False)
clf.fit(toy_features, toy_labels)
# print(clf.coef_) # coef_ is only available when using a linear kernel
print(clf.dual_coef_)
def test_algorithm_compare(self):
# -------------------------------------------------------------------------------
# # Problem 5
# #-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 100
L = 0.2
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
def plot_toy_results(algo_name, thetas):
print('theta for', algo_name, 'is', ', '.join(map(str, list(thetas[0]))))
print('theta_0 for', algo_name, 'is', str(thetas[1]))
utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)
plot_toy_results('Perceptron', thetas_perceptron)
plot_toy_results('Average Perceptron', thetas_avg_perceptron)
plot_toy_results('Pegasos', thetas_pegasos)
return
for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment'])
for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment'])
for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#
#-------------------------------------------------------------------------------
# Section 1.7
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 5
L = 10
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_avg_pa = p1.average_passive_aggressive(toy_features, toy_labels, T, L)
def plot_toy_results(algo_name, thetas):
utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)
plot_toy_results('Perceptron', thetas_perceptron)
plot_toy_results('Average Perceptron', thetas_avg_perceptron)
# You may modify the following when adding additional features (Part 3c)
bigram_dictionary = lab2.bigram_dictionary(train_texts)
train_final_features = lab2.extract_final_features(train_texts, dictionary,
bigram_dictionary)
val_final_features = lab2.extract_final_features(val_texts, dictionary,
bigram_dictionary)
test_final_features = lab2.extract_final_features(test_texts, dictionary,
bigram_dictionary)
#-------------------------------------------------------------------------------
# Part 1 - Perceptron Algorithm
#-------------------------------------------------------------------------------
toy_features, toy_labels = utils.load_toy_data('../../Data/toy_data.csv')
theta, theta_0 = lab2.perceptron(toy_features, toy_labels, T=5)
utils.plot_toy_results(toy_features, toy_labels, theta, theta_0)
#-------------------------------------------------------------------------------
# Part 2 - Classifying Reviews
#-------------------------------------------------------------------------------
theta, theta_0 = lab2.perceptron(train_bow_features, train_labels, T=5)
train_accuracy = lab2.accuracy(train_bow_features, train_labels, theta,
theta_0)
val_accuracy = lab2.accuracy(val_bow_features, val_labels, theta, theta_0)
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment']) for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment']) for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#
#-------------------------------------------------------------------------------
#
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 5
L = 10
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_avg_pa = p1.average_passive_aggressive(toy_features, toy_labels, T, L)
def plot_toy_results(algo_name, thetas):
utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)
plot_toy_results('Perceptron', thetas_perceptron)
plot_toy_results('Average Perceptron', thetas_avg_perceptron)
plot_toy_results('Average Passive-Aggressive', thetas_avg_pa)
def main(unused_argv):
# proposal_hidden_dims = [20]
# likelihood_hidden_dims = [0]
with tf.Graph().as_default():
if FLAGS.dataset in ["mnist", "struct_mnist"]:
train_xs, valid_xs, test_xs = utils.load_mnist()
elif FLAGS.dataset == "omniglot":
train_xs, valid_xs, test_xs = utils.load_omniglot()
elif FLAGS.dataset == "toy":
train_xs, valid_xs, test_xs = utils.load_toy_data()
print("dataset = ", train_xs.shape)
# Placeholder for input mnist digits.
# observations_ph = tf.placeholder("float32", [None, 2])
observations_ph = tf.placeholder("float32", [None, 1])
# set up your prior dist, proposal and likelihood networks
(prior, likelihood,
proposal) = model.get_toy_models(train_xs, which_example="toy1D")
# Compute the lower bound and the loss
estimators = model.iwae(
prior,
likelihood,
proposal,
observations_ph,
FLAGS.num_samples,
[], # [alpha, beta, gamma, delta],
contexts=None)
print("VARS: ", proposal.fcnet.get_variables())
log_p_hat, neg_model_loss, neg_inference_loss = estimators[
FLAGS.estimator]
elbo = estimators["elbo"]
model_loss = -tf.reduce_mean(neg_model_loss)
inference_loss = -tf.reduce_mean(neg_inference_loss)
log_p_hat_mean = tf.reduce_mean(log_p_hat)
# this is over K samples
print("INFERENCE LOSS SHAPE = ", neg_inference_loss.shape)
model_params = prior.get_parameter_mu()
print(model_params)
inference_network_params = proposal.fcnet.get_variables()
# Compute and apply the gradients, summarizing the gradient variance.
global_step = tf.train.get_or_create_global_step()
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
cv_grads = []
model_grads = opt.compute_gradients(model_loss, var_list=model_params)
# inference model (encoder) params are just A and b. (Ax+b)
inference_grads = opt.compute_gradients(
inference_loss, var_list=inference_network_params)
grads = model_grads + inference_grads #+ cv_grads
model_ema_op, model_grad_variance, _ = (
utils.summarize_grads(model_grads))
print("grads = ", grads)
inference_ema_op, inference_grad_variance, inference_grad_snr_sq = (
utils.summarize_grads(inference_grads))
ema_ops = [model_ema_op, inference_ema_op]
# this ensures you evaluate ema_ops before the apply_gradient function :)
with tf.control_dependencies(ema_ops):
train_op = opt.apply_gradients(grads, global_step=global_step)
# l2_norm = lambda t: tf.sqrt(tf.reduce_sum(tf.pow(t, 2)))
# for gradient, variable in inference_grads:
# tf.summary.scalar("phi_grads/" + variable.name, l2_norm(gradient))
# tf.summary.scalar("phi_vars/" + variable.name, l2_norm(variable))
# tf.summary.histogram("phi_grads/" + variable.name, gradient)
# tf.summary.histogram("phi_vars/" + variable.name, variable)
tf.summary.scalar("estimators/elbo", elbo)
tf.summary.scalar("estimators/difference", elbo - log_p_hat_mean)
# tf.summary.scalar("phi_grad/%s" % FLAGS.estimator, inference_ema_op)
tf.summary.scalar("phi_grad_variance/%s" % FLAGS.estimator,
inference_grad_variance)
tf.summary.scalar("model_grad", model_grad_variance)
# tf.summary.scalar("inference_grad_snr_sq/%s" % FLAGS.estimator, inference_grad_snr_sq)
tf.summary.scalar("log_p_hat/train", log_p_hat_mean)
exp_name = "%s.lr-%g.n_samples-%d.batch_size-%d.alpha-%g.dataset-%s.run-%d" % (
FLAGS.estimator, FLAGS.learning_rate, FLAGS.num_samples,
FLAGS.batch_size, FLAGS.alpha, FLAGS.dataset, FLAGS.run)
checkpoint_dir = os.path.join(FLAGS.logdir, FLAGS.subfolder, exp_name)
print("Checkpoints: : ", checkpoint_dir)
if FLAGS.initial_checkpoint_dir and not tf.gfile.Exists(
checkpoint_dir):
tf.gfile.MakeDirs(checkpoint_dir)
f = "checkpoint"
tf.gfile.Copy(os.path.join(FLAGS.initial_checkpoint_dir, f),
os.path.join(checkpoint_dir, f))
with tf.train.MonitoredTrainingSession(
is_chief=True,
hooks=
[
create_logging_hook({
"Step": global_step,
"log_p_hat": log_p_hat_mean,
# "model_grads": model_grad,
# "model_grad_variance": model_grad_variance,
"infer_grad_varaince": inference_grad_variance,
"infer_grad_snr_sq": inference_grad_snr_sq,
})
],
checkpoint_dir=checkpoint_dir,
save_checkpoint_secs=10,
save_summaries_steps=FLAGS.summarize_every,
# log_step_count_steps=FLAGS.summarize_every * 10
log_step_count_steps=
0, # disable logging of steps/s to avoid TF warning in validation sets
# config=tf.ConfigProto(log_device_placement=True) # spits out the location of each computation (CPU, GPU etc.)
) as sess:
writer = summary_io.SummaryWriterCache.get(checkpoint_dir)
t_stats = []
cur_step = -1
indices = list(range(train_xs.shape[0]))
n_epoch = 0
def run_eval(cur_step, split="valid", eval_batch_size=256):
"""Run evaluation on a datasplit."""
if split == "valid":
eval_dataset = valid_xs
elif split == "test":
eval_dataset = test_xs
log_p_hat_vals = []
for i in range(0, eval_dataset.shape[0], eval_batch_size):
# batch_xs = utils.binarize_batch_xs(eval_dataset[i:(i + eval_batch_size)])
batch_xs = eval_dataset[i:(i + eval_batch_size)]
log_p_hat_vals.append(
sess.run(log_p_hat_mean,
feed_dict={observations_ph: batch_xs}))
summary = tf.Summary(value=[
tf.Summary.Value(tag="log_p_hat/%s" % split,
simple_value=np.mean(log_p_hat_vals))
])
writer.add_summary(summary, cur_step)
print("curr_step: %g, log_p_hat/%s: %g" %
(cur_step, split, np.mean(log_p_hat_vals)))
while cur_step < FLAGS.max_steps and not sess.should_stop():
n_epoch += 1
random.shuffle(indices)
for i in range(0, train_xs.shape[0], FLAGS.batch_size):
if sess.should_stop() or cur_step > FLAGS.max_steps:
break
# Get a batch, then dynamically binarize
ns = indices[i:i + FLAGS.batch_size]
# batch_xs = utils.binarize_batch_xs(train_xs[ns])
batch_xs = train_xs[ns]
_, cur_step, grads_ = \
sess.run([train_op, global_step, grads], feed_dict={observations_ph: batch_xs})
# grads_ = sess.run([train_op, global_step, model_params, grads], feed_dict={observations_ph: batch_xs})
if n_epoch % 10 == 0:
print("epoch #", n_epoch)
run_eval(cur_step, "test")
run_eval(cur_step, "valid")
# var_names = ["theta", "A ", "b "]
var_names = ["A ", "b "]
for m, (i, j) in enumerate(grads_):
print(var_names[m], ": grad, val: ", i, j)
def toy_example(num_samples=None, noise=(0,0)):
if num_samples==None:
num_samples = FLAGS.num_samples
with tf.GradientTape() as tape:
train_xs, valid_xs, test_xs = utils.load_toy_data()
batch_xs = train_xs[0:FLAGS.batch_size] # [batch_size, input_dim]
# set up your prior model, proposal and likelihood networks
p_z = ToyPrior(mu_inital_value = 2., size=FLAGS.latent_dim, name="toy_prior")
# returns a callable Normal distribution
p_x_given_z = ToyConditionalNormalLikelihood()
# with tf.name_scope('proposal') as scope:
q_z = ToyConditionalNormal(
size=FLAGS.latent_dim,
hidden_layer_sizes=1,
initializers=None,
use_bias=True,
name="proposal")
# initialise the network parameters to optimal (plus some specified N dist'ed noise)
q_z.initialise_and_fix_network(batch_xs, noise)
# returns the Normal dist proposal, and the parameters (fixed to optimal A and b)
proposal, inference_network_params = q_z(batch_xs, stop_gradient=False)
z = proposal.sample(sample_shape=[num_samples])
# [num_samples, batch_size, latent_dim]
print("z samples ", z.shape)
# returns a Normal dist conditioned on z
likelihood = p_x_given_z(z)
# returns the Prior normal (p_z), and the prior parameter mu
prior, mu = p_z()
log_p_z = tf.reduce_sum(prior.log_prob(z), axis=-1) # [num_samples, batch_size]
log_q_z = tf.reduce_sum(proposal.log_prob(z), axis=-1) # [num_samples, batch_size]
log_p_x_given_z = tf.reduce_sum(likelihood.log_prob(batch_xs), axis=-1) # [num_samples, batch_size]
log_weights = log_p_z + log_p_x_given_z - log_q_z # [num_samples, batch_size]
# This step is crucial for replicating the IWAE bound. log of the sum, NOT sum of the log (the VAE bound - where M increases)
log_sum_weight = tf.reduce_logsumexp(log_weights, axis=0) # this sums over K samples, and returns us to IWAE estimator land
log_avg_weight = log_sum_weight - tf.log(tf.to_float(num_samples))
inference_loss = -tf.reduce_mean(log_avg_weight)
# print("shapes", log_p_z.shape, log_p_x_given_z.shape, log_q_z.shape, log_weights.shape, log_sum_weight.shape, inference_loss.shape)
parameters = (inference_network_params[0], inference_network_params[1], mu)
# print("near optimal parameters: ", parameters)
grads = tape.gradient(inference_loss, parameters)
# Build the evidence lower bound (ELBO) or the negative loss
# kl = tf.reduce_mean(tfd.kl_divergence(proposal, prior), axis=-1) # analytic KL
# log_sum_ll = tf.reduce_logsumexp(log_p_x_given_z, axis=0) # this converts back to IWAE estimator (log of the sum)
# expected_log_likelihood = log_sum_ll - tf.log(tf.to_float(num_samples))
# KL_elbo = tf.reduce_mean(expected_log_likelihood - kl)
if FLAGS.using_BQ:
def get_log_joint(z):
return np.reshape(p_x_given_z(z).log_prob(batch_xs).numpy() + prior.log_prob(z).numpy(), (-1, 1))
kernel = GPy.kern.RBF(1, variance=2, lengthscale=2)
kernel.variance.constrain_bounded(1e-5, 1e5)
bq_likelihood = GPy.likelihoods.Gaussian(variance=1e-1)
bq_prior = Gaussian(mean=proposal._loc.numpy().squeeze(), covariance=proposal._scale.numpy().item())
initial_x = bq_prior.sample(5)
initial_y = []
for point in initial_x:
initial_y.append(get_log_joint(np.atleast_2d(point)))
initial_y = np.concatenate(initial_y)
mean_function = NegativeQuadratic(1)
gpy_gp = GPy.core.GP(initial_x, initial_y, kernel=kernel, likelihood=bq_likelihood, mean_function=mean_function)
warped_gp = VanillaGP(gpy_gp)
bq_model = IntegrandModel(warped_gp, bq_prior)
for i in range(10):
if i % 5 == 0:
gpy_gp.optimize_restarts(num_restarts=5)
failed = True
while failed:
try:
batch = select_batch(bq_model, 1, KRIGING_BELIEVER)
failed = False
except FloatingPointError:
gpy_gp.optimize_restarts(num_restarts=5)
X = np.array(batch)
Y = get_log_joint(X)
bq_model.update(batch, Y)
gpy_gp.optimize_restarts(num_restarts=5)
bq_elbo = bq_model.integral_mean()
import scipy.integrate
def integrand(z):
return get_log_joint(z) * np.exp(bq_prior.logpdf(np.atleast_2d(z)))
brute_force_elbo = scipy.integrate.quad(integrand, -10, 10)
print("BQ ", bq_elbo)
print("ACTUAL ELBO ", brute_force_elbo)
return grads
val_texts, val_labels = zip(*((sample['text'], sample['sentiment'])
for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment'])
for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#-------------------------------------------------------------------------------
# Calculate theta & theta_0 using each algorithm
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data(
cwd + '\\Review-Analyzer\\toy_data.tsv')
T = 10
L = 0.2
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
def plot_toy_results(algo_name, thetas):
print('theta for', algo_name, 'is', ', '.join(map(str, list(thetas[0]))))
print('theta_0 for', algo_name, 'is', str(thetas[1]))
utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)