def copy_into_process_base(process_base):
if not fi.exists(process_base):
fi.mkdir(process_base)
os.system("cp %s %s" % (command, process_base))
os.system("cp %s %s" % (fi.join(autophrase_base, 'phrasal_segmentation.sh'), process_base))
os.system("cp -r %s %s" % (fi.join(autophrase_base, 'bin'), process_base))
os.system("cp -r %s %s" % (fi.join(autophrase_base, 'data'), process_base))
os.system("cp -r %s %s" % (fi.join(autophrase_base, 'tools'), process_base))
def set_base_path(self, base_path):
self.base_path = fi.add_sep_if_needed(base_path)
for path in [
self.get_base_path(),
self.get_theme_path(),
self.get_queried_path(),
self.get_param_path(),
]:
fi.mkdir(path)
def write_cic_list(path, cic_list):
"""
调用 cic_list 中的各元素的 construct_od返回一个OrderedDIct，将每个OrderedDIct持久化到指定路径下的文件中
:param path: str，输出路径
:param cic_list: list，每个元素为 ClusterInfoCarrier
:return:
"""
fi.mkdir(path, remove_previous=True)
cic_list = sorted(cic_list, key=lambda item: len(item.twarr), reverse=True)
print(' bext: output cic list, len={}'.format(len(cic_list)))
for idx, cic in enumerate(cic_list):
cluid = cic.cluid
cic.twarr = ClusterInfoGetter.group_similar_tweets(cic.twarr,
process_num=10)
od = cic.construct_od()
json_str = fu.dumps(od)
cluster_file = fi.join(path, '{}_cluid:{}.json'.format(idx, cluid))
fu.write_lines(cluster_file, [json_str])
# textarr_file = fi.join(path, '{}_text.json'.format(idx))
# textarr = [tw[tk.key_text] for tw in cic.twarr]
# fu.write_lines(textarr_file, textarr)
print(' bext: output into files over')
ft_model = "/home/nfs/cdong/tw/src/models/classify/terror/ft_no_gpe_model"
lr_model = "/home/nfs/cdong/tw/src/models/classify/terror/lr_no_gpe_model"
clf_model = lr_model
tmu.check_time('all')
tmu.check_time()
# coef_of_lr_model("/home/nfs/cdong/tw/src/models/classify/terror/lr_no_gpe_model")
# clf_filter = ClassifierAddFeature(None, None)
# for file in fi.listchildren("/home/nfs/cdong/tw/seeding/Terrorist/queried/positive", concat=True):
# twarr = fu.load_array(file)
# print(file, clf_filter.predict_mean_proba(twarr))
# tmu.check_time()
# exit()
batch_num = 20
fi.mkdir(ft_data_pattern.format('matrices_no_add'), remove_previous=True)
train_mtx_ptn = ft_data_pattern.format(
'matrices_no_add/train_feature_mtx_{}.npy')
train_lbl_ptn = ft_data_pattern.format(
'matrices_no_add/train_lblarr_mtx_{}.npy')
train_file_list = [(train_mtx_ptn.format(idx), train_lbl_ptn.format(idx))
for idx in range(batch_num)]
# _clf = ClassifierAddFeature(None, None)
# _ft_args = dict(epoch=150, lr=1.5, wordNgrams=2, verbose=2, minCount=2, thread=20, dim=300)
# tmu.check_time()
# _clf.train_ft(fasttext_train, _ft_args, ft_model)
# tmu.check_time(print_func=lambda dt: print('train ft time: {}s'.format(dt)))
# generate_train_matrices(ft_model, fasttext_train, train_file_list)
# tmu.check_time()
import shutil
from subprocess import Popen, PIPE
from config.configure import getcfg
import utils.function_utils as fu
import utils.array_utils as au
import utils.file_iterator as fi
import utils.multiprocess_utils as mu
import utils.tweet_keys as tk
import utils.timer_utils as tmu
autophrase_base = getcfg().autophrase_path
autophrase_output_base = fi.join(
autophrase_base, "OUTPUTS/") # 保证任何autophrase的输出限制到output_base之内的某个角落
command = fi.join(autophrase_base, "auto_phrase.sh")
fi.mkdir(autophrase_output_base)
def autophrase(input_text_file, output_path, commander, process_base, min_sup):
p = Popen(commander,
shell=True,
bufsize=1,
stdin=PIPE,
stdout=PIPE,
stderr=PIPE,
cwd=process_base)
p.stdin.flush()
p.stdin.write((input_text_file + '\n').encode("utf8"))
p.stdin.write((output_path + '\n').encode("utf8"))
p.stdin.write((min_sup + '\n').encode("utf8"))
p.stdin.flush()
return
print('new list generated, hbn={}, rbn={}'.format(hbn, rbn))
cluid_twarr_list = self.gsdpmm.get_cluid_twarr_list(twnum_thres)
if cluid_twarr_list:
out_channel.put(cluid_twarr_list)
if __name__ == '__main__':
import utils.tweet_keys as tk
import utils.array_utils as au
import utils.pattern_utils as pu
import utils.timer_utils as tmu
import calling.back_extractor as bext
import utils.file_iterator as fi
import utils.function_utils as fu
fi.mkdir('/home/nfs/cdong/tw/src/calling/tmp', remove_previous=True)
tmu.check_time()
_hold_batch_num = 100
_batch_size = 100
_alpha, _beta = 30, 0.01
# _alpha, _beta = 50, 0.005
_file = "./filtered_twarr.json"
_twarr = fu.load_array(_file)[:10200]
start_pool(_hold_batch_num, _batch_size, _alpha, _beta)
input_twarr_batch(_twarr)
print('---> waiting for _cluid_cluster_list')
while True:
_cluid_cluster_list = cluster_daemon.outq2.get()
print(' - some thing returned, type :{}'.format(
class ExtractSubProcess(DaemonProcess):
def start(self, func):
self.process = mp.Process(target=func, args=(self.inq, self.outq))
self.process.daemon = False
self.process.start()
# print('ExtractSubProcess', self.process.pid)
extract_sub_process = ExtractSubProcess()
END_PROCESS = -1
SET_PARAMS = 0
INPUT_LIST = 1
OUTPUT_LIST = 3
OUT_BASE = getcfg().output_path
fi.mkdir(OUT_BASE, remove_previous=True)
def extract_sub_main(inq, outq):
"""
聚类信息提取模块子进程的主函数，该进程还有下属的若干子进程用于执行实际操作，
负责读取主进程输入，调用子进程组进行一次地点提取，根据返回结果，对多个聚类依地点进行合并操作，
并再次调用子进程组对合并结果进行完整处理，以该结果为最终结果输出到文件
:param inq: mp.Queue，主进程向子进程的输入队列
:param outq: mp.Queue，子进程向主进程的输出队列
:return:
"""
pool_size, event_type = inq.get()
extract_pool = CustomDaemonPool()
extract_pool.start(extract_pool_main, pool_size)
extract_pool.set_batch_input([(pidx, event_type)
mtx_list.append(np.load(mtx_file))
lbl_list.append(np.load(lbl_file))
featurearr = np.concatenate(mtx_list, axis=0)
labelarr = np.concatenate(lbl_list, axis=0)
return featurearr, labelarr
if __name__ == "__main__":
from classifying.natural_disaster.nd_data_maker import nd_train, nd_test, nd_data_pattern
nd_ft_model = "/home/nfs/cdong/tw/src/models/classify/natural_disaster/ft_model"
nd_lr_model = "/home/nfs/cdong/tw/src/models/classify/natural_disaster/lr_model"
tmu.check_time('all')
tmu.check_time()
batch_num = 20
fi.mkdir(nd_data_pattern.format('matrices'))
nd_train_mtx = nd_data_pattern.format('matrices/train_feature_mtx_{}.npy')
nd_train_lbl = nd_data_pattern.format('matrices/train_lblarr_mtx_{}.npy')
nd_train_mtx_files = [(nd_train_mtx.format(idx), nd_train_lbl.format(idx))
for idx in range(batch_num)]
_clf = ClassifierNaturalDisaster(None, None)
# _ft_args = dict(epoch=150, lr=1.5, wordNgrams=2, verbose=2, minCount=2, thread=20, dim=250)
# _clf.train_ft(nd_train, _ft_args, nd_ft_model)
# tmu.check_time(print_func=lambda dt: print('train ft time: {}s'.format(dt)))
generate_train_matrices(nd_ft_model, nd_train, nd_train_mtx_files)
tmu.check_time()
_featurearr, _labelarr = recover_train_matrix(nd_train_mtx_files)
tmu.check_time()
_lr_args = dict(n_jobs=20,
