def analyze_glda_result():
glda_base = '/home/cdong/works/research/clu/baselinee/GaussianLDA'
out_dir_ = iu.join(glda_base, 'output_{}/')
d_classes = (DataReuters(), Data20ng())
# for d in d_classes:
# # topic_list = d.get_topic_list()
# out_dir = out_dir_.format(d.name)
# # for out_dir in fi.listchildren(glda_base, children_type=fi.TYPE_DIR, pattern='output'):
# # print(out_dir)
# for param in fi.listchildren(out_dir, children_type=fi.TYPE_DIR, concat=True):
# print(param[param.rfind('/') + 1:])
# for assignment in fi.listchildren(param, concat=True, pattern='table_assignments_'):
# lines = fu.read_lines(assignment)
# print(len(lines))
from collections import Counter
import utils.array_utils as au
import re
def fn(f):
return f[f.rfind('/') + 1:]
for out_dir in iu.list_children(glda_base,
ctype=iu.DIR,
pattern='^output',
full_path=True):
# topic_list = Data20ng().get_topic_list()
dname = re.findall('output_(.+)$', out_dir)[0]
print(dname)
topic_list = name2object[dname].get_topics()
print(out_dir)
for param in iu.list_children(out_dir, ctype=iu.DIR, full_path=True):
print(param[param.rfind('/') + 1:])
for assign in iu.list_children(param,
pattern='table_assignments',
full_path=True):
lines = iu.read_lines(assign)
if len(lines) != len(topic_list):
continue
print(fn(assign), len(lines))
print(lines[100])
cluster_list = [
Counter(list(map(int,
line.split_length()))).most_common()[0][0]
for line in lines
]
print(au.score(topic_list, cluster_list, 'nmi'))
print(au.score(topic_list, cluster_list, 'ari'))
def test(test_file, model_file):
textarr, labelarr = list(), list()
with open(test_file) as testfp:
lines = testfp.readlines()[:20]
for line in lines:
label, text = line.strip().split(' ', 1)
textarr.append(text)
labelarr.append(label)
# for idx, line in enumerate(testlines):
# if pu.is_empty_string(line):
# continue
# label, text = line.split(' ', 1)
# print(label, model.predict(text, threshold=0.5), text)
pred_value_arr = predict(textarr, ftu.load_model(model_file))
label = [label2value[label] for label in labelarr]
print(au.score(label, pred_value_arr, 'auc'))
def LECM_twarr_with_label(twarr, tw_cluster_label):
# Currently best hyperparam 1, 0.1, 0.1, 1
# tw_topic_arr, tw_cluster_pred = LECMClusterer.LECM_twarr(twarr, 1, 0.1, 0.1, 1, 20, 1)
# print('one epoch:alpha {:<5}, eta {:<5}, beta {:<5}, lambd {:<5}, NMI {:<8}\n'.
# format(0.1, 0.1, 0.1, 0.1, au.score(tw_cluster_pred, tw_cluster_label, 'nmi')))
tw_topic_arr = tw_cluster_pred = nmi = 0
for alpha in [1]:
for eta in [0.1]:
for beta in [0.1]:
for lambd in [1]:
tw_topic_arr_, tw_cluster_pred_ = LECMClusterer.LECM_twarr(twarr, alpha, eta, beta, lambd, 20, 70)
nmi_ = au.score(tw_cluster_pred_, tw_cluster_label, 'nmi')
print('alpha {:<5}, eta {:<5}, beta {:<5}, lambd {:<5}, NMI{:<8}'.
format(alpha, eta, beta, lambd, nmi_))
if nmi < nmi_:
tw_topic_arr, tw_cluster_pred = tw_topic_arr_, tw_cluster_pred_
nmi = nmi_
return tw_topic_arr, tw_cluster_pred
def test(test_file, model_file):
textarr, labelarr = list(), list()
with open(test_file) as testfp:
lines = testfp.readlines()
for line in lines:
label, text = line.strip().split(' ', 1)
textarr.append(text)
labelarr.append(label)
preds, scores = predict(textarr, threshold=0.2)
assert len(preds) == len(textarr)
for thres in [i/10 for i in range(2, 11)]:
print(thres, Counter([1 if s > thres else 0 for s in scores]))
label = [label2value[label] for label in labelarr]
print(au.score(label, preds, 'auc'))
for idx in range(1000, 1100):
pred, lb, text = preds[idx], label[idx], textarr[idx]
if not pred == lb:
print(pred, lb, text)
def analyze_refine_mean_and_stderr(result_file, mean_std_file):
using_scores = ['nmi', 'h**o', 'cmplt', 'ari']
arg_tpc_clu_list = iu.load_array(result_file)
rows = list()
for kwargs, topics, clusters in arg_tpc_clu_list:
scores = [au.score(topics, clusters, s) for s in using_scores]
res_dict = Od(zip(using_scores, scores))
row = Od(list(kwargs.items()) + list(res_dict.items()))
rows.append(row)
rows = sorted(rows, key=lambda item: item['nmi'], reverse=True)
df = pd.DataFrame(data=rows)
print(df)
score_array = df[using_scores].values
mean = np.mean(score_array, axis=0)
std = np.std(score_array, axis=0, ddof=1)
table = list(zip(*[using_scores, mean, std]))
lines = [
'{}: {} ± {}'.format(name, round(mean, 4), round(std, 4))
for name, mean, std in table
]
iu.write_lines(mean_std_file, lines)
def input_twarr_with_label(twarr, label):
# alpha_range = beta_range = [i/100 for i in range(1, 10, 3)] + [i/10 for i in range(1, 10, 3)] + \
# [i for i in range(1, 10, 3)]
# K_range = [30, 40, 50]
alpha_range = beta_range = [i / 100 for i in range(1, 10, 4)
] + [i / 10 for i in range(1, 10, 4)]
K_range = [30, 40, 50]
"""cluster using different hyperparams in multiprocess way"""
iter_num = 100
process_num = 20
hyperparams = [(a, b, K) for a in alpha_range for b in beta_range
for K in K_range]
res_list = list()
for i in range(int(math.ceil(len(hyperparams) / process_num))):
param_list = [
(twarr, *param, iter_num)
for param in hyperparams[i * process_num:(i + 1) * process_num]
]
res_list += utils.multiprocess_utils.multi_process(
GSDMM.GSDMM_twarr, param_list)
print('{:<4} /'.format((i + 1) * process_num), len(hyperparams),
'params processed')
"""group the data by K"""
frame = pd.DataFrame(index=np.arange(0, len(hyperparams)),
columns=['alpha', 'beta', 'K'])
for i in range(len(hyperparams)):
frame.loc[i] = hyperparams[i]
print('\n', frame, '\n')
"""start plotting figures"""
for (alpha, K), indices in frame.groupby(['alpha',
'K']).groups.items():
fig = plt.figure()
fig.set_figheight(8)
fig.set_figwidth(8)
all_nmi = list()
for i in indices:
beta = frame.loc[i]['beta']
tw_cluster_pred_iter = res_list[i]
iter_x = range(len(tw_cluster_pred_iter))
nmi_y = [
au.score(label, pred, 'nmi')
for pred in tw_cluster_pred_iter
]
all_nmi.append(nmi_y)
plt.plot(iter_x,
nmi_y,
'-',
lw=1.5,
label='beta={}'.format(round(beta, 2)))
plt.xlabel('iteration')
plt.ylabel('NMI')
plt.ylim(0.0, 0.75)
plt.title('K=' + str(K))
plt.legend(loc='lower right')
plt.grid(True, '-', color='#333333', lw=0.8)
plt.text(iter_num - 40,
0.70,
'final nmi: ' +
str(round(max([nmi[-1] for nmi in all_nmi]), 6)),
fontsize=14,
verticalalignment='bottom',
horizontalalignment='left')
plt.savefig(getcfg().dc_test + 'GSDMM/' +
'alpha={},K={}.png'.format(round(alpha, 2), K))
for i in range(3):
hist = model.fit(X,
B,
validation_split=0.1,
epochs=100,
batch_size=batch_size,
verbose=0,
shuffle=True)
# create model that gives penultimate layer
inputs = model.layers[0].input
outputs = model.layers[-2].output
model_penultimate = Model(inputs, outputs)
# inference of penultimate layer
H = model_penultimate.predict(X)
V = normalize(H, norm='l1')
print("Sample shape: {}".format(H.shape))
# n_clusters = len(np.unique(y))
# print("Number of classes: %d" % n_clusters)
km = KMeans(n_clusters=clu_num, n_jobs=4, max_iter=200)
km.fit(V)
pred = km.labels_
# nmi = cluster_quality(y, pred)
d = dict([(s, au.score(y, pred, s)) for s in ['nmi', 'ari']])
print(d)
# logger.info(entries2name(d, inter=' ', intra=':', postfix=''))
# np.save("pred.npy", pred)
# model.save_weights("model.plk")
