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potts_sample.py
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potts_sample.py
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import argparse
import toy_data
import rbm
import torch
import numpy as np
import samplers
import mmd
import torch.nn as nn
import matplotlib.pyplot as plt
import os
import torchvision
device = torch.device('cuda:' + str(0) if torch.cuda.is_available() else 'cpu')
import utils
import tensorflow_probability as tfp
import block_samplers
import time
import pickle
def makedirs(dirname):
"""
Make directory only if it's not already there.
"""
if not os.path.exists(dirname):
os.makedirs(dirname)
def get_ess(chain, burn_in):
print(chain.shape)
c = chain
l = c.shape[0]
bi = int(burn_in * l)
c = c[bi:]
cv = tfp.mcmc.effective_sample_size(c).numpy()
print(cv)
cv[np.isnan(cv)] = 1.
return cv
def main(args):
makedirs(args.save_dir)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
model = rbm.LatticePottsModel(int(args.dim), int(args.n_out), args.sigma, args.bias)
model.to(device)
print(device)
if args.n_out == 3:
plot = lambda p, x: torchvision.utils.save_image(x.view(x.size(0), args.dim, args.dim, 3).transpose(3, 1),
p, normalize=False, nrow=int(x.size(0) ** .5))
else:
plot = None
ess_samples = model.init_sample(args.n_samples).to(device)
hops = {}
ess = {}
times = {}
chains = {}
temps = ['dim-gibbs', 'rand-gibbs', 'gwg']
for temp in temps:
if temp == 'dim-gibbs':
sampler = samplers.PerDimMetropolisSampler(args.dim ** 2, args.n_out)
elif temp == "rand-gibbs":
sampler = samplers.PerDimMetropolisSampler(args.dim ** 2, args.n_out, rand=True)
elif "bg-" in temp:
block_size = int(temp.split('-')[1])
sampler = block_samplers.BlockGibbsSampler(model.data_dim, block_size)
elif "hb-" in temp:
block_size, hamming_dist = [int(v) for v in temp.split('-')[1:]]
sampler = block_samplers.HammingBallSampler(model.data_dim, block_size, hamming_dist)
elif temp == "gwg":
sampler = samplers.DiffSamplerMultiDim(args.dim ** 2, 1, approx=True, temp=2.)
elif "gwg-" in temp:
n_hops = int(temp.split('-')[1])
sampler = samplers.MultiDiffSampler(model.data_dim, 1,
approx=True, temp=2., n_samples=n_hops)
else:
raise ValueError("Invalid sampler...")
x = model.init_dist.sample((args.n_test_samples,)).to(device)
times[temp] = []
hops[temp] = []
chain = []
cur_time = 0.
for i in range(args.n_steps):
# do sampling and time it
st = time.time()
xhat = sampler.step(x.detach(), model).detach()
cur_time += time.time() - st
# compute hamming dist
cur_hops = (x != xhat).float().view(x.size(0), -1).sum(-1).mean().item()
# update trajectory
x = xhat
if i % args.subsample == 0:
if args.ess_statistic == "dims":
chain.append(x.cpu()[0].view(-1).numpy()[None])
else:
xc = x[0][None]
h = (xc != ess_samples).float().view(ess_samples.size(0), -1).sum(-1)
chain.append(h.detach().cpu().numpy()[None])
if i % args.viz_every == 0 and plot is not None:
plot("/{}/temp_{}_samples_{}.png".format(args.save_dir, temp, i), x)
if i % args.print_every == 0:
times[temp].append(cur_time)
hops[temp].append(cur_hops)
print("temp {}, itr = {}, hop-dist = {:.4f}".format(temp, i, cur_hops))
chain = np.concatenate(chain, 0)
chains[temp] = chain
ess[temp] = get_ess(chain, args.burn_in)
print("ess = {} +/- {}".format(ess[temp].mean(), ess[temp].std()))
ess_temps = temps
plt.clf()
plt.boxplot([ess[temp] for temp in ess_temps], labels=ess_temps, showfliers=False)
plt.savefig("{}/ess.png".format(args.save_dir))
plt.clf()
plt.boxplot([ess[temp] / times[temp][-1] / (1. - args.burn_in) for temp in ess_temps], labels=ess_temps, showfliers=False)
plt.savefig("{}/ess_per_sec.png".format(args.save_dir))
plt.clf()
for temp in temps:
plt.plot(hops[temp], label="{}".format(temp))
plt.legend()
plt.savefig("{}/hops.png".format(args.save_dir))
for temp in temps:
plt.clf()
plt.plot(chains[temp][:, 0])
plt.savefig("{}/trace_{}.png".format(args.save_dir, temp))
with open("{}/results.pkl".format(args.save_dir), 'wb') as f:
results = {
'ess': ess,
'hops': hops,
'chains': chains
}
pickle.dump(results, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', type=str, default="/tmp/test_discrete")
parser.add_argument('--data', choices=['mnist', 'random'], type=str, default='random')
parser.add_argument('--n_steps', type=int, default=5000)
parser.add_argument('--n_samples', type=int, default=500)
parser.add_argument('--n_test_samples', type=int, default=100)
parser.add_argument('--gt_steps', type=int, default=10000)
parser.add_argument('--seed', type=int, default=8008135)
# model def
parser.add_argument('--dim', type=int, default=10)
parser.add_argument('--n_out', type=int, default=3)
parser.add_argument('--sigma', type=float, default=.1)
parser.add_argument('--bias', type=float, default=0.)
# logging
parser.add_argument('--print_every', type=int, default=10)
parser.add_argument('--viz_every', type=int, default=100)
# for rbm training
parser.add_argument('--rbm_lr', type=float, default=.001)
parser.add_argument('--cd', type=int, default=10)
parser.add_argument('--img_size', type=int, default=28)
parser.add_argument('--batch_size', type=int, default=100)
# for ess
parser.add_argument('--subsample', type=int, default=1)
parser.add_argument('--burn_in', type=float, default=.1)
parser.add_argument('--ess_statistic', type=str, default="dims", choices=["hamming", "dims"])
args = parser.parse_args()
main(args)