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main_dc.py
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main_dc.py
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import os, time, datetime
import sys
import torch
import torch.optim as optim
from tqdm import tqdm, trange
from torchvision import transforms, datasets
from torchvision.utils import make_grid
import argparse
from argparse import Namespace
from nice import NICE
from dircluster import sample_mu_lam, SSE, LLH
from utils import savefig_clusters, mvnlogpdf, plt2img
from AE import AE
import matplotlib
# import matplotlib.pyplot as plt
import numpy as np
from numpy import log, exp, pi
from scipy.stats import wishart, gamma
from scipy.stats import multivariate_normal as normal
from numpy.linalg import inv, det
from matplotlib.patches import Ellipse
from scipy.special import loggamma
import tensorflow as tf
from scipy.stats import wishart, gamma, entropy
# from plot_cf import pretty_plot_confusion_matrix
import pandas as pd
from sklearn.metrics.cluster import v_measure_score, adjusted_rand_score, pair_confusion_matrix, normalized_mutual_info_score, adjusted_mutual_info_score
# matplotlib.use('Agg')
from ae_util import *
from utils import *
TRAIN_BATCH_SIZE = 128
def parse_args(manual_args=None):
parser = argparse.ArgumentParser(description='main.')
# dataset params
parser.add_argument('--dataset', type=str, default='mnist')
parser.add_argument('--dim', type=int, default=10) # todo 以前默认是768, 会默认算到超参里面去,影响?
parser.add_argument('--noise', type=float, default=0.1)
parser.add_argument('--n_sample_load', type=int, default=10000)
# n_iter params
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--iter_dmm', type=float, default=[2, 1, 0.5], nargs='*')
parser.add_argument('--dmm_rebuild_freq', type=int, default=50)
parser.add_argument('--iter_nice', type=float, default=[2, 1, 0.5], nargs='*')
parser.add_argument('--save_freq', type=int, default=20)
# core hyper parameters
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--maxK', type=int, default=500)
parser.add_argument('--logalpha', type=float, default=0.0)
parser.add_argument('--a0', type=float, default=50.0)
parser.add_argument('--b0', type=float, default=10.0)
parser.add_argument('--kappa0', type=float, default=1.0)
parser.add_argument('--decaycoef', type=float, default=0.9)
parser.add_argument('--decaymin', type=float, default=0.1)
parser.add_argument('--contralam', type=float, default=1.0)
parser.add_argument('--reglam', type=float, default=1.0)
parser.add_argument('--hinge', type=float, default=100.0)
parser.add_argument('--figpath', type=str, default='./res2')
parser.add_argument('--pretrainpath', type=str, default='./saved_ae/mnist_ae_best428.pt')
parser.add_argument('--aex_file', type=str, default='./saved_ae/mnist_ae_feat.pt')
# additional params
parser.add_argument('--exp_name', type=str, default='exp')
parser.add_argument('--a_stage_steps', type=int, default=[-1, -1], nargs='*')
parser.add_argument('--a_stage_values', type=float, default=[0, 0], nargs='*')
parser.add_argument('--with_flow', type=str, default='yes')
parser.add_argument('--restart_dmm', type=str, default='no')
parser.add_argument('--restart_flow', type=str, default='no')
parser.add_argument('--refresh_cc_in_flow', type=str, default='no')
parser.add_argument('--grad_clip', type=float, default=1) # grad norm clipping
parser.add_argument('--loss_clip', type=float, default=1) # grad norm clipping
parser.add_argument('--log_dir', type=str, default='./exp_out')
parser.add_argument('--nice_nlayers', type=int, default=6)
parser.add_argument('--nice_units', type=int, default=512)
parser.add_argument('--input_normalize', type=str, default='no')
parser.add_argument('--device', type=str, default='cpu')
args, args_list = parser.parse_known_args(args=manual_args)
# param check
args.input_normalize = True if args.input_normalize == 'yes' else False
assert len(args.a_stage_steps) == len(args.a_stage_values)
args.a_stage_steps = np.asarray(args.a_stage_steps)
args.a_stage_values = np.asarray(args.a_stage_values)
# init necessary arguments
if not os.path.exists(args.figpath):
os.makedirs(args.figpath)
print(args)
return args
def get_iter_num(n_epoch, n_samples, cfg_list):
'''
How many steps for iteration in the n-th epoch
num_iter is configed to decay for fast training
'''
ratio = cfg_list[-1] if n_epoch >= len(cfg_list) else cfg_list[n_epoch]
return int(np.ceil(n_samples * ratio))
def cluster2label(K, samples_k, y):
map_dict = {}
y_pred = samples_k.copy()
for k_idx in range(K): # analysis each cluster
cluster_sid = (samples_k == k_idx)
label, counts = np.unique(y[cluster_sid], return_counts=True)
major_class_idx = np.argmax(counts)
map_dict[k_idx] = label[major_class_idx]
y_pred[cluster_sid] = label[major_class_idx]
return y_pred, map_dict
def pair_f1_score(ys, ks):
cf = pair_confusion_matrix(ys, ks)
# TN, FP
# FN, TP
return cf[1, 1] / float(cf[1, 1] + 0.5 * (cf[0, 1] + cf[1, 0]))
def get_count_matrix(K, cluster_label, nC, label):
# count labels in each cluster
np.unique(cluster_label)
cluster_counts_list = []
for k in range(K):
c_sample_id = (cluster_label == k)
y, ny = np.unique(label[c_sample_id], return_counts=True)
counts_arr = np.zeros(nC)
counts_arr[y] = ny
cluster_counts_list.append(counts_arr)
count_matrix = np.stack(cluster_counts_list, axis=0)
return count_matrix
def cluster_acc_fixed(Y_pred, Y): # from paper VaDE code
from scipy.optimize import linear_sum_assignment as linear_assignment
assert Y_pred.size == Y.size
D = max(Y_pred.max(), Y.max()) + 1
w = np.zeros((D, D), dtype=np.int64)
for i in range(Y_pred.size):
w[Y_pred[i], Y[i]] += 1
a, b = linear_assignment(w.max() - w)
ind = [(a[i], b[i]) for i in range(a.shape[0])]
return sum([w[i, j] for i, j in ind]) * 1.0 / Y_pred.size, ind
def eval_cluster(samples_k, ys, return_extra=False, alg='default', ds_name='default'):
cids, nK = np.unique(samples_k, return_counts=True)
k_indices = np.argsort(-nK) # desc sort by size
k_major, k_entropy = [], []
confusion_matrix = np.zeros(shape=(10, 10))
correct = 0
for k_idx in k_indices: # analysis each cluster
cluster_sid = (samples_k == cids[k_idx])
cluster_size = nK[k_idx]
label, counts = np.unique(ys[cluster_sid], return_counts=True)
k_entropy.append(entropy(counts / cluster_size))
major_class_idx = np.argmax(counts)
k_major.append(label[major_class_idx])
confusion_matrix[label, label[major_class_idx]] += counts
correct += counts[major_class_idx]
acc = correct / np.sum(nK)
weighted_entropy = np.sum(nK[k_indices]/np.sum(nK) * k_entropy)
# k-irrevlent measure
v_score, adj_rand = v_measure_score(ys, samples_k), adjusted_rand_score(ys, samples_k),
pair_f1 = pair_f1_score(ys, samples_k)
if not return_extra:
# print(f'{alg} on {ds_name}:\n\t F score | V score | ARI | ACC* | K | wEntropy :\n\t'
# f'{pair_f1:.4f} & {v_score:.4f} & {adj_rand:.4f} & {acc:.4f} & {len(cids)} & {weighted_entropy:.4f}')
return acc, v_score, adj_rand, pair_f1, weighted_entropy, confusion_matrix
else:
nmi = normalized_mutual_info_score(ys, samples_k)
# print(f'{alg} on {ds_name}:\n\t F score | V score | ARI | NMI | ACC* | K | wEntropy :\n\t'
# f'{pair_f1:.4f} & {v_score:.4f} & {adj_rand:.4f} & {nmi:.4f} & {acc:.4f} & {len(cids)} & {weighted_entropy:.4f}')
return pair_f1, v_score, adj_rand, nmi, acc, len(cids), weighted_entropy, confusion_matrix
def dirichlet_clustering(global_epoch, tb_writter, dir_params, samples, ys, n_iter_samples, args, log_step=0):
# samples contains the full dataset, but used sample is indicated by n_sample_load
# extract variables
hp = dir_params.hyper
_mu0, _ka0, logalpha, _a0, _b0 = hp.mu0, hp.ka0, hp.logalpha, hp.a0, hp.b0
K, lam_K, mu_K, nK, ks = dir_params.K, dir_params.lam_K, dir_params.mu_K, dir_params.n_K, dir_params.samples_k
# begin iter
# random_samples_idx_subset = np.random.randint(0, len(ks), n_iter_samples)
monitor_freq = 100
with tb_writter.as_default():
for iter_idx in range(n_iter_samples):
sample_idx = iter_idx % len(ks) # mod by n_sample_load
# dynamic alpha
if args.a_stage_steps[0] != -1: # when enabled
logalpha = args.a_stage_values[np.where(iter_idx >= args.a_stage_steps)[0][-1]]
## report result
global_log_step = iter_idx+log_step
# others
if (global_log_step + 1) % monitor_freq == 0:
# report cluster information
tf.summary.scalar('dmm/K', K, step=iter_idx+log_step)
arr = np.asarray(nK)/sum(nK)
tf.summary.histogram('cluster size ratio', arr, step=global_log_step)
mu_norm = np.linalg.norm(np.asarray(mu_K), axis=1)
tf.summary.histogram('cluster mu norm', mu_norm, step=global_log_step)
tf.summary.histogram('cluster lambda', np.asarray(lam_K), step=global_log_step)
# report metrics
if (global_log_step + 1) % (monitor_freq*10) == 0:
error = SSE(samples, K, ks, nK, mu_K, lam_K)
pair_f1, v_score, adj_rand, nmi, acc, _, w_ent, _ = eval_cluster(ks, ys,return_extra=True, ds_name=args.dataset)
tf.summary.scalar('dmm/sse', error, step=global_log_step)
tf.summary.scalar('dmm/acc', acc, step=global_log_step)
tf.summary.scalar('dmm/v_score', v_score, step=global_log_step)
tf.summary.scalar('dmm/nmi', nmi, step=global_log_step)
tf.summary.scalar('dmm/adj_rand', adj_rand, step=global_log_step)
tf.summary.scalar('dmm/pair_f1', pair_f1, step=global_log_step)
tf.summary.scalar('dmm/w_entropy', w_ent, step=global_log_step)
tb_writter.flush()
# if (global_log_step + 1) % args.n_sample_load == 0:
# # show confusion matrix
# results = eval_cluster(nK, ks, ys)
# cf = results[-1]
# pllt, fig = pretty_plot_confusion_matrix(pd.DataFrame(cf, index=range(10), columns=range(10)))
# cf_img = plt2img(pllt, fig)
# tf.summary.image('confusion matrix', cf_img, step=global_log_step)
# # show top 15 clusters
# # img = savefig_clusters(global_epoch, K, ks, ds_ae, samples_flow_z,
# # mu_K, path=args.figpath, datasetname=args.dataset,
# # cluster_idx_lst=np.argsort(arr)[-1:-15:-1])
# # tf.summary.image('cluster info', img, step=global_log_step)
# tb_writter.flush()
# executing DPM
xi = samples[sample_idx]
old_k = ks[sample_idx]
if old_k != -1:
# remove x[n], re-label if cluster becomes empty
# 用最后一个位置填空位
nK[old_k] -= 1
if nK[old_k] == 0: # 删除空簇
idx = (ks == K - 1)
ks[idx] = old_k
nK[old_k], lam_K[old_k], mu_K[old_k], = nK[K - 1], lam_K[K - 1], mu_K[K - 1]
nK, lam_K, mu_K = nK[:-1], lam_K[:-1], mu_K[:-1]
K -= 1
# 3.1.1 Sampling Cluster Assingment k
p_lst = [] # p_lst[k] is the probability that xi in C_k, p_lst[-1] means xi forms a new cluster
Kn = min(K, args.maxK) # 最大运算类别数限制,dirichlet可能刚开始生产的过多
if K == 0: # if no cluster, just build a new one
chosen_k = 0
else:
# existing cluster assignment prob
Klst = np.random.choice(K, size=Kn, replace=False, p=np.asarray(nK) / np.sum(np.asarray(nK)))
for k in Klst:
pk = log(nK[k]) + mvnlogpdf(xi, mu_K[k], 1 / lam_K[k])
p_lst.append(pk)
# new cluster assignment pob.
_kan = _ka0 + 1
_an = _a0 + 0.5 * args.dim
_bn = _b0 + _ka0 * np.linalg.norm(xi - _mu0) ** 2 / (2 * (_ka0 + 1))
logpk = log(logalpha) + \
loggamma(_an) - loggamma(_a0) + \
_a0 * log(_b0) - _an * log(_bn) + \
0.5 * (log(_ka0) - log(_kan)) - args.dim / 2 * log(2 * pi)
p_lst.append(logpk)
# sampling according to the assignment prob.
maxpk = max(p_lst)
p_lst = [exp(v - maxpk) for v in p_lst] # 防止数值溢出
chosen_k = np.random.choice(list(range(Kn + 1)),
p=p_lst / sum(p_lst)) # sample, now xi belongs cluster chosen_k
# 3.1.2 Sampling Cluster mean and lambda
if chosen_k == Kn: # assigned to new cluster
nK.append(1)
ks[sample_idx] = K
lam_k, mu_k = sample_mu_lam(samples, nK, ks, chosen_k, _mu0, _ka0, _a0, _b0)
mu_K.append(mu_k)
lam_K.append(lam_k)
K += 1
else: # assigned to existing cluster
chosen_k = Klst[chosen_k]
nK[chosen_k] += 1
ks[sample_idx] = chosen_k
# 过一段时间再实际更新簇中心点的参数
if sample_idx % args.dmm_rebuild_freq == 0 or sample_idx == args.n_sample_load:
for k in range(K):
lam_K[k], mu_K[k] = sample_mu_lam(samples, nK, ks, k, _mu0, _ka0, _a0, _b0)
# the last iter
pass
tf.summary.scalar('dmm_epoch/sse', error, step=global_epoch)
tf.summary.scalar('dmm_epoch/acc', acc, step=global_epoch)
tf.summary.scalar('dmm_epoch/v_score', v_score, step=global_epoch)
tf.summary.scalar('dmm_epoch/adj_rand', adj_rand, step=global_epoch)
tf.summary.scalar('dmm_epoch/pair_f1', pair_f1, step=global_epoch)
tf.summary.scalar('dmm_epoch/nmi', nmi, step=global_epoch)
tf.summary.scalar('dmm_epoch/w_entropy', w_ent, step=global_epoch)
# update params
dir_params.K, dir_params.lam_K, dir_params.mu_K, dir_params.n_K, dir_params.samples_k = K, lam_K, mu_K, nK, ks
return dir_params
# 每轮从0开始,每轮延续,每轮刷新?
def update_cluster_center_by_repr(samples_flow_z, dir_params):
"""
after each epoch, repr is updated by flow, but the mean & lam are calced by old repr.
maybe we need to `refresh` them with new repr
:param samples_flow_z:
:param dir_params:
:return:
"""
hp = dir_params.hyper
_mu0, _ka0, logalpha, _a0, _b0 = hp.mu0, hp.ka0, hp.logalpha, hp.a0, hp.b0
K, lam_K, mu_K, nK, ks = dir_params.K, dir_params.lam_K, dir_params.mu_K, dir_params.n_K, dir_params.samples_k
for k in range(K):
lam_K[k], mu_K[k] = sample_mu_lam(samples_flow_z, nK, ks, k, _mu0, _ka0, _a0, _b0)
dir_params.lam_K, dir_params.mu_K, = lam_K, mu_K
return dir_params
def train_flow(global_epoch, tb_writter, model_nice, opt, ds_inf_iter, n_iter, dir_params, args,
log_step=0, prev_z_repr=None):
if n_iter == 0:
print('flow_opt=0, train ignored')
return
model_nice = model_nice.to(args.device)
epoch_n_batch = args.n_sample_load // TRAIN_BATCH_SIZE
epoch_loss, epoch = 0.0, 0
K, ks, ns, mu_K, lam_K = dir_params.K, dir_params.samples_k, dir_params.n_K, dir_params.mu_K, dir_params.lam_K
model_nice.to(args.device)
with tb_writter.as_default():
for n_batch in range(n_iter):
(_, x, _, idx) = next(ds_inf_iter)
x = x.to(args.device)
# 1. calc loss
model_nice.train()
opt.zero_grad()
# x = x.view(-1, args.dim) # + (torch.rand(784)-0.5) / 256.
x = x.to(args.device)
_, likelihood = model_nice(x, K, ks[idx.numpy()], ns, np.asarray(mu_K), np.asarray(lam_K), args.reglam,
args.contralam, args.hinge)
if isinstance(likelihood, int):
continue
loss = -torch.mean(likelihood) # NLL
# 2. report real loss
tf.summary.scalar('flow/batch_nll_loss', loss.detach().cpu().numpy(), step=n_batch + global_epoch * n_iter)
if (n_batch + 1) % epoch_n_batch == 0:
tb_writter.flush()
tf.summary.scalar('flow/epoch_nll_loss', epoch_loss / epoch_n_batch,
step=int(epoch + global_epoch * (n_iter / epoch_n_batch)))
epoch_loss *= 0.0
epoch += 1
else:
epoch_loss += loss.detach().cpu().numpy()
# 3. cliping and applying gradients
if args.loss_clip > 0:
loss = torch.clip(loss, max=args.loss_clip)
loss.backward()
if args.grad_clip > 0:
torch.nn.utils.clip_grad_norm_(model_nice.parameters(), args.grad_clip)
opt.step()
# refresh cluster center
if (n_batch + 1) % epoch_n_batch == 0:
if args.refresh_cc_in_flow == 'yes':
dir_params = update_cluster_center_by_repr(prev_z_repr, dir_params)
K, ks, ns, mu_K, lam_K = dir_params.K, dir_params.samples_k, dir_params.n_K, dir_params.mu_K, dir_params.lam_K
def init_dir_params(args):
dir_params = Namespace(
hyper=Namespace(
a0=args.a0 * args.dim, b0=args.b0 * args.dim,
mu0=np.zeros(args.dim), ka0=args.kappa0,
logalpha=args.logalpha
),
K=0, n_K=[], lam_K=[], mu_K=[], samples_k=np.ones(args.n_sample_load, dtype=int) * -1
# K: total num of clusters. lam_K, mu_K: sampled mu/lambda cluster. n_K: size of clusters
# samples_k: cluster index for each sample, -1 means unassigned
)
return dir_params
def init_flow_model(args):
model_nice = NICE(data_dim=args.dim, num_coupling_layers=args.nice_nlayers,
num_hidden_units=args.nice_units, device_name=args.device)
opt = optim.Adam(model_nice.parameters(), args.lr)
return model_nice, opt
def load_ae_dataset(ds_name, n_sample_load, aex_file):
print('start loading ae dataset')
if ds_name == 'mnist':
ds_raw = load_dataset(ds_name, './data', split='train')
ds_ae_repr = torch.load(aex_file)[:n_sample_load]
ds_ae = AEDataset(ds_raw, ds_ae_repr, n_sample_load)
elif 'VaDE_' in ds_name:
X, Y, Z = np.load(f'../VaDE/{ds_name}_full_X.npz')['arr_0'], \
np.load(f'../VaDE/{ds_name}_full_Y.npz')['arr_0'].reshape(-1, ), \
np.load(f'../VaDE/{ds_name}_full_Z.npz')['arr_0']
# X = (X - X.mean()) / X.std() # todo critical norm
ds_ae_repr = torch.from_numpy(Z)[:n_sample_load]
ds_ae = OtherAEDataset(X, Y, Z, n_sample_load)
else:
raise ValueError(f'dataset name {ds_name} not supported')
return ds_ae_repr, ds_ae
def main(manual_args=None):
args = parse_args(manual_args)
time_str = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
exp_id = '%s_%s_N%d_E%d_kappa0_%s_a0_%s_b0_%s_alpha_%s_%s_p%s' % (
args.exp_name, args.dataset, args.n_sample_load, args.epoch, args.kappa0, args.a0, args.b0, args.logalpha, time_str, os.getpid())
task_dir = os.path.join(args.log_dir, exp_id)
if not os.path.exists(task_dir):
os.mkdir(task_dir)
# 1. load AE dataset
ds_ae_repr, ds_ae = load_ae_dataset(args.dataset, args.n_sample_load, args.aex_file)
dl_ae = InfiniteDataLoader(dataset=ds_ae, batch_size=TRAIN_BATCH_SIZE, shuffle=True, pin_memory=True)
iter_ae_inf = iter(dl_ae)
print(f'data loaded, cls dist: N={args.n_sample_load}'
f'{np.stack(np.unique(ds_ae.targets, return_counts=True))}')
# 2. init model nice
model_nice, opt = init_flow_model(args)
# 3. init dirichlet mixture model
dir_params = init_dir_params(args)
# 4.alternated training
tb_logger = tf.summary.create_file_writer(task_dir)
with tb_logger.as_default():
tf.summary.text('config', str(args), step=0)
tf.summary.text('flow arch', str(model_nice), step=0)
tf.summary.histogram('total distance norm init AE', torch.norm(ds_ae_repr, dim=1).cpu().numpy(), step=-1)
tb_logger.flush()
global_step_dmm, global_step_flow = 0, 0
for n_epoch in range(args.epoch):
print(f'{os.getpid()}-DDPM training epoch {n_epoch}')
# 1. update sample representation
# to_use_flow = None if args.with_flow == 'no' or n_epoch == 0 else model_nice
# 第一轮用不用flow的影响不大 (reuters10k上简单测验了下)
# samples_flow_z = get_flow_repr(ds_ae_repr, model_nice, args.device, normalize=args.input_normalize)
samples_flow_z = transform_z(model_nice, ds_ae, args.n_sample_load)
# samples_flow_z = samples_flow_z.detach().cpu().numpy()
with tb_logger.as_default():
distance = np.linalg.norm(samples_flow_z, axis=1)
tf.summary.histogram('total distance norm flow', distance, step=n_epoch)
distance_normed = np.linalg.norm((samples_flow_z-samples_flow_z.mean(axis=0)) / samples_flow_z.std(axis=0), axis=1)
tf.summary.histogram('total distance norm flow (normed)', distance_normed, step=n_epoch)
tb_logger.flush()
if args.refresh_cc_in_flow == 'yes' and n_epoch >= 1: # refresh cluster center repr with updated samples repr
update_cluster_center_by_repr(samples_flow_z, dir_params)
# 2. dirichlet clustering
# if args.restart_dmm == 'yes':
# dir_params = init_dir_params(args)
n_iter_clst = get_iter_num(n_epoch, args.n_sample_load, args.iter_dmm)
dir_params = dirichlet_clustering(n_epoch, tb_logger, dir_params, samples_flow_z, ds_ae.targets, n_iter_clst, args, log_step=global_step_dmm)
global_step_dmm += n_iter_clst+1
# 3. flow model trainging
if args.with_flow == 'yes':
if args.restart_flow == 'yes':
model_nice, opt = init_flow_model(args)
n_iter_opt = int(np.ceil(get_iter_num(n_epoch, args.n_sample_load, args.iter_nice) / TRAIN_BATCH_SIZE))
train_flow(n_epoch, tb_logger, model_nice, opt, iter_ae_inf, n_iter_opt, dir_params, args,
log_step=global_step_flow, prev_z_repr=samples_flow_z)
global_step_flow += n_iter_opt+1
else:
print('without flow opt')
# save params
if (n_epoch+1) % args.save_freq == 0 or n_epoch == args.epoch-1:
# save flow model
torch.save(model_nice.state_dict(), os.path.join(task_dir, f'flow_E{n_epoch}.pt'))
# save dpm params
np.savez(os.path.join(task_dir, f'dpm_E{n_epoch}'), K=dir_params.K, ks=dir_params.samples_k, ns=dir_params.n_K,
mu_K=np.asarray(dir_params.mu_K), lam_K=np.asarray(dir_params.lam_K))
if __name__ == '__main__':
main()