def predict(cfg, net, model_path):
mode = cfg['mode']
device = cfg['device']
class_num = cfg['class_num']
batch_size = cfg['batch_size']
num_workers = cfg['num_workers']
dataset_path = cfg['dataset_path']
model_name = net.__class__.__name__
if len(cfg['gpu_ids']) > 1:
model_name = net.module.__class__.__name__
# output_dir = os.path.join(cfg['output_dir'], model_name, splitext(split(model_path)[1])[0])
# if os.path.exists(output_dir):
# shutil.rmtree(output_dir)
# os.makedirs(output_dir,exist_ok=True)
current_time = datetime.datetime.now()
logger_file = os.path.join('log', mode, '{} {}.log'.
format(model_name, current_time.strftime('%Y-%m-%d %H-%M-%S')))
logger = setup_logger(f'{model_name} {mode}',logger_file)
dataset = SelfDataset(os.path.join(dataset_path, 'data_test.npy'), os.path.join(dataset_path, 'label_test.npy'), logger)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
net.load_state_dict(torch.load(model_path, map_location=device))
net.eval()
test_loss, acc, f1, corr = eval(cfg, net, loader, device, 'Test')
logger.info('Test Mean accuracy: {:.6f} F-Score: {:.6f} Correlation Coefficient: {:.6f}'.format(acc, f1, corr))
def frame_and_publish(topic, key, event):
# logger
pub_logger = setup_logger('pub_log', 'logs/publisher_log.log', logging.DEBUG)
pub_logger.info("New publishment starts")
producer = connect_kafka_producer()
print('Trying to open camera')
vidcap = cv2.VideoCapture(0)
print('Opened camera by index 0 \n\n')
success, image = vidcap.read()
count = 0
while success:
# capture frame
if event.is_set():
break
success, image = vidcap.read()
#publish to kafka topic
try:
key_bytes = bytes(key, encoding='utf-8')
ret, buffer = cv2.imencode('.jpg', image)
producer.send(topic, key=key_bytes, value=buffer.tobytes())
producer.flush()
pub_logger.debug('Image {} has been published successfully'.format(count))
count += 1
except Exception as ex:
pub_logger.debug('Exception in publishing message')
pub_logger.debug(str(ex))
# close the publisher
if producer is not None:
producer.close()
def __init__(self,chatID,raw_message):
#Instantiate the command library from the parent class with Super!
self.commands_dict = super(ActiveConversation, self).get_commands_dict()
# Instantiate properties with the ChatId and Message
self.chatID = chatID
self.active = True
self.ActualMessage = raw_message
#Instantiate phase indicator and get Unique id
self.conversation_phase = 0 #For multiple-phase conversations
self.uniqueID = uuid.uuid4().get_hex()
#Set conversation logger.
newlogger = tools.setup_logger(self.uniqueID,os.path.dirname(os.path.realpath(__file__))
+'/logs/'+str(self.chatID)+'.log')
self.logger = logging.getLogger(self.uniqueID)
#Set error counter
self.errorcounter = 0
#Classify the creation command. Do we need the chat engine or we know the message command?
if self.commandsQ(raw_message):
self.function = self.AssignCommand(raw_message)
self.logger.info('Conversation marked as command.')
self.function_type = 'BotCommand'
else:
self.function = None
self.function_type = 'ChatEngine'
self.logger.info('Conversation marked as chat.')
self.cache =[]
def consume_save(display_topic, offset, db_info, event):
# logger
sav_logger = setup_logger('sav_log', 'logs/saver_log.log', logging.DEBUG)
sav_logger.info("New saving starts")
# init db connection
db = pymysql.connect(db_info['host'], db_info['user'], db_info['passwd'],
db_info['db'])
cursor = db.cursor()
# consume predicted sentiment
consumer = KafkaConsumer(display_topic,
auto_offset_reset=offset,
bootstrap_servers=['localhost:9092'],
api_version=(0, 10),
consumer_timeout_ms=1000)
# consume one by one and save the result to db
flag = True
num_trials = 5
while flag:
if event.is_set():
break
for pred in consumer:
if event.is_set():
flag = False
break
pred = pred.value
try:
pred = int(pred.decode("utf-8"))
# save sentiment
query = "INSERT INTO PRED (pred) VALUES ({})".format(pred)
cursor.execute(query)
db.commit()
sav_logger.debug('Sucessfully saved sentiment {}'.format(pred))
except Exception as ex:
sav_logger.debug('failed')
sav_logger(ex)
db.rollback()
num_trials -= 1
consumer.close()
db.close()
def train(cfg, net):
lr = cfg['lr']
mode = cfg['mode']
device = cfg['device']
weight = cfg['weight']
batch_size = cfg['batch_size']
num_workers = cfg['num_workers']
dataset_path = cfg['dataset_path']
model_name = net.__class__.__name__
current_time = datetime.datetime.now()
logger_file = os.path.join('log', mode, '{} {} lr {} bs {} ep {}.log'.
format(model_name,current_time.strftime('%Y-%m-%d %H-%M-%S'),
cfg['lr'], cfg['batch_size'], cfg['epochs']))
logger = setup_logger(f'{model_name} {mode}',logger_file)
writer = SummaryWriter(log_dir=os.path.join('runs', model_name))
dataset = SelfDataset(os.path.join(dataset_path, 'data_train.npy'), os.path.join(dataset_path, 'label_train.npy'), logger)
loader = DataLoader(dataset)
n_val = int(len(dataset) * cfg['valid_percent'] / 100)
n_train = len(dataset) - n_val
train_dataset, val_dataset = random_split(dataset, [n_train, n_val])
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, pin_memory=True, drop_last=False)
data, label = iter(train_loader).next()
data = data.to(device)
label = label.to(device)
writer.add_graph(net, data)
display_weight = weight
weight = torch.tensor(weight)
if device == 'cuda':
weight = weight.cuda()
criterion = CrossEntropyLoss(weight=weight)
optimizer = Adam(net.parameters(), lr=cfg['lr'], betas=(0.9,0.999))
# optimizer = SGD(net.parameters(), lr=cfg['lr'], momentum=0.9)
logger.info(f'''Starting training:
Model: {net.__class__.__name__}
Epochs: {cfg['epochs']}
Batch size: {cfg['batch_size']}
Learning rate: {cfg['lr']}
Training size: {n_train}
Weight: {display_weight}
Validation size: {n_val}
Device: {device}
''')
iter_num = 0
train_batch_num = len(train_dataset) // batch_size
max_acc = 0
max_f1 = 0
max_corr = 0
best_epoch = 0
for epoch in range(1, cfg['epochs'] + 1):
net.train()
epoch_loss = []
logger.info('epoch[{}/{}]'.format(epoch,cfg['epochs']))
with tqdm(total=n_train, desc='Epoch {}/{}'.format(epoch,cfg['epochs']),unit='items') as pbar:
# for iter, (data, label) in enumerate(train_loader):
for data, label in train_loader:
data = data.to(device)
label = label.to(device)
predict = net(data)
loss = criterion(predict, torch.argmax(label, dim=1)) # CrossEntropy
# loss = criterion(predict, label) # MSE
loss_item = loss.item()
epoch_loss.append(loss_item)
pbar.set_postfix(**{'loss (batch)':loss_item})
optimizer.zero_grad()
loss.backward()
# nn.utils.clip_grad_value_(net.parameters(), 0.1)
optimizer.step()
pbar.update(data.shape[0])
iter_num += 1
if iter_num % (train_batch_num//10) == 0:
# acc, f1, corr = metric(torch.argmax(label, dim=1).data.cpu().numpy(), torch.argmax(predict, dim=1).data.cpu().numpy())
with torch.no_grad():
acc, f1, corr = metric(label.data.cpu().numpy(), F.softmax(predict, dim=1).data.cpu().numpy())
logger.info('Training Loss: {:.6f} Mean accuracy: {:.6f} F-Score: {:.6f} Correlation Coefficient: {:.6f}'.format(loss_item, acc, f1, corr))
writer.add_scalar('Training Loss', loss_item, iter_num)
writer.add_scalar('Training Mean accuracy', acc, iter_num)
writer.add_scalar('Training F-Score', f1, iter_num)
writer.add_scalar('Training Correlation Coefficient', corr, iter_num)
net.eval()
val_loss, acc, f1, corr = eval(cfg, net, val_loader, device, 'Validation')
logger.info('Validation Loss: {:.6f} Mean accuracy: {:.6f} F-Score: {:.6f} Correlation Coefficient: {:.6f}'.format(val_loss, acc, f1, corr))
# writer.add_scalar('Validation Loss', val_loss, epoch)
writer.add_scalar('Validation Mean accuracy', acc, epoch)
writer.add_scalar('Validation F-Score', f1, epoch)
writer.add_scalar('Validation Correlation Coefficient', corr, epoch)
if (acc > max_acc or (acc == max_acc and f1 > max_f1) or (acc == max_acc and f1 == max_f1 and corr > max_corr)) and (epoch > (cfg['epochs'] >> 2)):
max_acc = acc
max_f1 = f1
max_corr = corr
best_epoch = epoch
if not os.path.exists(cfg['checkpoint_dir']):
os.makedirs(cfg['checkpoint_dir'])
logger.info('Created checkpoint directory:{}'.format(cfg['checkpoint_dir']))
torch.save(net.state_dict(),os.path.join(cfg['checkpoint_dir'],"{}.pth".format(cfg['model'])))
logger.info(f'Checkpoint {epoch} saved!')
logger.info('Best epoch: {}/{} Mean accuracy: {:.6f} F-Score: {:.6f} Correlation Coefficient: {:.6f}'.format(best_epoch, cfg['epochs'], max_acc, max_f1, max_corr))
writer.close()
# -*- coding: UTF-8 -*-
from bs4 import BeautifulSoup
import copy
from datetime import datetime
import os
from pymongo import MongoClient
import re
import sys
from time import sleep
import urllib2
from tools import renew_ip, headers, setup_logger, exception_hook
logger = setup_logger('b92statistike-scrape_comments.log')
re_date_comment_extract = re.compile('([0-9]+).\s(.*)\s(201[456])\s([0-9]+):([0-9]+)')
def convert_month(monthstr):
"""
Converts month as string to something datetime module understands.
TODO: this sucks, dictionary is better
"""
if monthstr == 'januar':
return 1
elif monthstr == 'februar':
return 2
elif monthstr == 'mart':
return 3
elif monthstr == 'april':
def train_net():
# preparing
# args = Sina_News_Config()
args = Ren_CECps_Config()
processor = Processor(args)
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
time_stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
save_dir = join(args.output_dir, time_stamp)
os.makedirs(save_dir)
backup_code(os.path.abspath(os.path.curdir),
os.path.join(save_dir, "code"))
# set up logger
logger = setup_logger(save_path=join(save_dir, "logger.txt"))
_print = logger.info
tokenizer = BertTokenizer.from_pretrained(os.path.join(
args.output_dir, args.pretrained_model_name,
args.pretrained_model_name + "-vocab.txt"),
do_lower_case=False)
train_examples = processor.get_train_examples()
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
# Prepare model
model = ClassificationModel(
os.path.join(args.output_dir, args.pretrained_model_name),
args.num_labels, args.dropout_prob, args.hidden_size)
model.to(device)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
_print("Using multi GPUs!")
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
# Prepare dataloader
train_dataloader = make_dataloder(train_examples,
args.max_seq_length,
tokenizer,
args.train_batch_size,
mode="train")
test_examples = processor.get_test_examples()
with open("train.examples", "wb") as f:
pickle.dump(train_examples, f)
with open("test.examples", "wb") as f:
pickle.dump(test_examples, f)
test_dataloader = make_dataloder(test_examples,
args.max_seq_length,
tokenizer,
args.train_batch_size,
mode="test")
for epoch in trange(int(args.num_train_epochs), desc="Epoch", leave=True):
# tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
model.train()
for step, batch in enumerate(tqdm(train_dataloader,
desc="Training...")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, label_scores = batch
loss1, loss2 = model(input_ids, segment_ids, input_mask, label_ids,
label_scores)
# tr_loss += loss1.mean().item() + loss2.mean().item()
(loss1 + loss2).mean(dim=0).sum().backward()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
# do evaluation
model.eval()
clf_pred, clf_label, rk_pred, rk_label = [], [], [], []
for step, batch in enumerate(
tqdm(test_dataloader, desc="Evaluating...")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids, label_scores = batch
with torch.no_grad():
pred_logits, pred_scores = model(input_ids, segment_ids,
input_mask)
pred_logits = pred_logits.ge(0).float()
clf_pred.append(pred_logits.cpu().numpy())
clf_label.append(label_ids.cpu().numpy())
rk_pred.append(pred_scores.cpu().numpy())
rk_label.append(label_scores.cpu().numpy())
clf_pred, clf_label, rk_pred, rk_label = map(
lambda x: np.vstack(x), (clf_pred, clf_label, rk_pred, rk_label))
clf_result = evaluate_classification(clf_pred, clf_label)
rk_result = evaluate_ranking(rk_pred, rk_label, clf_label)
tb = pt.PrettyTable(field_names=list(clf_result.keys()) +
list(rk_result.keys()))
tb.add_row(list(clf_result.values()) + list(rk_result.values()))
tb.float_format = "0.4"
output_str = "\n" + tb.__str__()
tqdm.write(output_str)
_print(output_str)
torch.save(model.state_dict(),
join(save_dir, "model_{}.pt".format(epoch)))
import logging
from analyzer import *
import tools
import compiler
logger = tools.setup_logger('vm_logger', 'vm.log', logging.DEBUG)
from compiler import *
"""
Keep it simple
"""
MEM_SIZE=256 # Size of memory block
PROTECTED_SIZE=20 # Protected memory cannot be modified using code, just by mutation
GEN_REGS=8 # Number of general purpose registers
CODE_PCTG=0.25 # Percentage of memory extra-allocated to the Code Segment
STEPS_CYCLE=50 # Max number of steps in a run to avoid infinte loops or neverending programs
# Description of protected memory cells (only for the initial VM)
# These cells are the first positions of memory
# Offspringed VM is created from the values in the memory
PROTECTED=[
('ZERO',0),
('MEMSIZE',MEM_SIZE),
('PROTECTED',PROTECTED_SIZE),
('GENREGS',GEN_REGS),
('CODEPCTG',CODE_PCTG),
from bs4 import BeautifulSoup
from datetime import timedelta
from pymongo import MongoClient
import re
import sys
from time import sleep
from urllib2 import HTTPError
import urllib2
from tools import renew_ip, headers, setup_logger, exception_hook
import os
logger = setup_logger('b92statistike-scrape_news.log')
re_changing_publishing_time = re.compile('.*\|\s+([0-9][0-9]):([0-9][0-9])\s+->.*')
def _append_one_news_text(db, news_id, news_link, news_date_published):
"""
Reads news text for one news and updates it in Mongo.
It also updates news first date of publish (we don't have that on metadata fetch).
There is simple retry logic if we get any HTTP error. All pages are
saved locally and we use first those if they exists.
"""
if os.path.exists('dumps/news/n%s.html' % news_id):
logger.info('Found cached file %s, reusing it', news_id)
f = open('dumps/news/n%s.html' % news_id, 'r')
html_content = f.read()
f.close()
else:
if news_link.startswith('?'):
def consume_pred(raw_topic, offset, det_path, model_path, display_topic,
event):
# logger
pre_logger = setup_logger('pre_log', 'logs/predicter_log.log',
logging.DEBUG)
pre_logger.info("New prediction starts")
# producer to publish to display topic
producer = connect_kafka_producer()
# face detector to crop faces
detector = cv2.CascadeClassifier(det_path)
# load sentiment prediction model
model = get_model()
model.load_weights(model_path)
# consume raw images
consumer = KafkaConsumer(raw_topic,
auto_offset_reset=offset,
bootstrap_servers=['localhost:9092'],
api_version=(0, 10),
consumer_timeout_ms=1000)
key = bytes('display', encoding='utf-8')
# predict one by one and publish the predicted result
flag = True
num_trials = 5
while flag:
if num_trials == 0:
pre_logger.debug('Consumer has been idling for 5 times')
break
for im in consumer:
# terminate
if event.is_set():
# quit two layers of loop
flag = False
break
im = im.value
try:
im = np.frombuffer(im, dtype=np.int8)
im = cv2.imdecode(im, cv2.IMREAD_COLOR)
# predict
preds = detect_and_predict(detector, im, model)
# push to kafka topic
pre_logger.debug('{} faces detected in current frame'.format(
len(preds)))
for p in preds:
try:
p_bytes = bytes(str(p), encoding='utf-8')
producer.send(display_topic, key=key, value=p_bytes)
producer.flush()
pre_logger.debug(
'results {} predicted and published successfully'.
format(p))
except Exception as ex:
pre_logger.debug(
'A problem occurred when publishing to display topic'
)
pre_logger.debug(str(ex))
except Exception as ex:
pre_logger.debug('failed')
pre_logger.debug(ex)
num_trials -= 1
if producer is not None:
producer.close()
consumer.close()
from bs4 import BeautifulSoup
from datetime import datetime
from pymongo import MongoClient
import re
import sys
from time import sleep
from urllib2 import HTTPError
import urllib2
from tools import renew_ip, headers, setup_logger, exception_hook
import os
logger = setup_logger('b92statistike-scrape_newsmetadata.log')
re_extract_link = re.compile('(yyyy=([0-9]+))*(&mm=([0-9]+))*(&dd=([0-9]+))*(&nav_category=[0-9]+)*[&?]nav_id=([0-9]+)')
re_hour_minute = re.compile('([0-9][0-9]):([0-9][0-9])')
def _insert_news_metadata_category(db, category_id):
"""
Reads news metadata from one news category and insert them in Mongo.
We go back in time, first we need to skip news after 2015., then we
process those in 2015. and we know how to quit if we bump into news
which are earlier than 2015.
There is simple retry logic if we get any HTTP error. All pages are
saved locally and we use first those if they exists.
Function is completely idempotent.
"""
start = 0 # Start is URL parametar for B92 link
is_below_2015 = False
while(not is_below_2015):
_ = checks_extended.AbstractCheck
import os
import tempfile
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed
import osmapi
import requests
import simplejson
from jinja2 import Environment, PackageLoader
import tools
from engine import CheckEngine, Result
from sources.source_factory import SourceFactory
logger = tools.setup_logger(logging_level=logging.INFO)
def process_entity(entity, context):
"""
Takes one entity and performs all check with engine on it.
:param entity: Entity to check
:param context: Context
:return: List of all performed checks
"""
map_check = context['map-check']
cr = CheckEngine(map_check['checks'], entity, context)
return cr.check_all()
def generate_report(context, all_checks):
tools.red(' ooo oo ooo ') +
tools.yellow(' ') +
tools.green(' ooo oo ooo '))
print(
tools.red(' oooooooo ====================') +
tools.yellow('=====================================') +
tools.green('==================== oooooooo '))
print('')
start = timer()
# 1. get the command line arguments 'args' and all valid build combinations in the check directory from 'builds.ini'
args, builds = args_parser.getArgsAndBuilds()
# 2. set the logger 'log' with the debug level from 'args' to determine the level of logging which displays output to the user
tools.setup_logger(args.debug)
log = logging.getLogger('logger')
# 3. perform the regression check by a) building executables
# b) running the code
# c) performing the defined analyzes
check.PerformCheck(start, builds, args, log)
# 4. display the summary table with information for each build, run and analysis step
summary.SummaryOfErrors(builds, args)
# 5. display if regression check was successful or not and return the corresponding error code
summary.finalize(start, 0, check.Run.total_errors,
check.ExternalRun.total_errors, check.Analyze.total_errors,
check.Analyze.total_infos)
def main():
# Collect the arguments, if any were provided.
parser = tools.argument_parser()
args = parser.parse_args()
# Initiate logger settings
tools.setup_logger(log_level=args.log_level, log_type=args.log_type)
# Get the input data and convert it to a odered-list of dictionaries
csv_file = tools.OpenFile.gui_ask_open_csv()
csv_file = tools.OpenFile.process_csv(csv_file)
address_table = tools.table_to_dictionary(csv_file)
if len(address_table) == 0:
logging.warning("No data was provided.")
return {'status': False, 'data': None}
# Convert the address_table to a list of Address_FQDN objects
# first, clean up hostname data. remove fqdn and keep only hostname
for address in address_table:
address['device_hostname'] = _clean_device_hostname(
address['device_hostname'])
# If an interface name was provided, we'll convert it to a hostname
if 'interface_name' in address.keys():
if len(address['interface_name']) > 0:
try:
address['device_hostname'] = _clean_interface_hostname(
address['device_hostname'], address['interface_name'])
except Exception as error:
logging.warning(
f"Object data build failed on: {address['device_hostname']} - {address['ip_address']}\n{error}"
)
for i, address in enumerate(address_table):
address_data = {}
address_data.update({
'ipv4_address': address['ip_address'],
'hostname': address['device_hostname']
})
if 'domain' in address.keys():
address_data.update({'domain': address['domain']})
else:
address_data.update({'domain': None})
address_table[i] = address_data
address_objects = []
if s.MULTITHREAD:
with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
for address in address_table:
try:
executor.submit(_build_address_fqdn_object,
address_objects, address)
except Exception as error:
logging.warning(
f"Object data build failed on: {address['hostname']} - {address['ipv4_address']}\n{error}"
)
else:
for address in address_table:
try:
address_objects.append(Address_FQDN(**address))
except Exception as error:
logging.warning(
f"Object data build failed on: {address['hostname']} - {address['ipv4_address']}\n{error}"
)
# Reformat and save data to a spreadsheet
if s.SAVE_TO_CSV:
output_data = [[
"FQDN", "PTR", "IP Address", "FLU Exists", "FLU Existing Value",
"FLU Needs Update", "RLU Exists", "RLU Existing Value",
"RLU Needs Update"
]]
for obj in address_objects:
output_data.append([
obj.full_name, obj.ptr_record, obj.ip_address,
obj.forward_lookup_exists, obj.forward_lookup_existing_value,
obj.forward_lookup_needs_update, obj.reverse_lookup_exists,
obj.reverse_lookup_existing_value,
obj.reverse_lookup_needs_update
])
tools.SaveFile.gui_ask_save_csv(output_data)
return {'status': True, 'data': address_objects}
from bs4 import BeautifulSoup
import sys
from urllib2 import HTTPError
import urllib2
from tools import renew_ip, setup_logger, exception_hook, headers
logger = setup_logger('b92statistike-dump_categories.log')
if __name__ == '__main__':
"""
This program reads all pages where categories are defined and prints them out.
TODO: I used this only when data was in SQL, but even then I manually created inserts
out of this dumped output. Should be more automatic, but it was one-time thing.
"""
sys.excepthook = exception_hook
renew_ip()
for i in range(1, 2000):
url = 'http://www.b92.net/info/vesti/index.php?&nav_category={}'.format(i)
logger.info('Fetching url %s', url)
retries = 0
try:
request = urllib2.Request(url, None, headers)
response = urllib2.urlopen(request)
except HTTPError as e:
logger.warning('Error during fetching')
if retries == 3:
raise e
else:
retries = retries + 1
type=str,
help="Path of testing data")
parser.add_argument("--model_path",
default='./data/sgns.sogounews.bigram-char',
type=str,
help="Path of Word2Vec model")
parser.add_argument("--dim",
default=300,
type=int,
help="Dimension of word vector")
args = parser.parse_args()
logger_file = os.path.join(
'log', 'preprocess',
'{}.log'.format(datetime.datetime.now().strftime('%Y-%m-%d %H-%M-%S')))
logger = setup_logger('preprocess', logger_file)
logger.info(f'''===> Preprocessing <===
Train Path: {args.train_path}
Test Path: {args.test_path}
Model Path: {args.model_path}
Output Path: {'./dataset'}
Dimension: {args.dim}
''')
logger.info('Loading Word2Vec model')
model = load_model(args.model_path)
logger.info('Loading Word2Vec model completed!')
logger.info('Prepocessing training dataset')
divide_dataset(path=args.train_path,
# -*- coding: UTF-8 -*-
import emoji
from pymongo import MongoClient
import pymssql
import sys
from tools import setup_logger, exception_hook
DB_SERVER = '<add-your-server>.database.windows.net'
DB_USERNAME = '******'
DB_PASSWORD = '******'
DB_NAME = 'b92'
logger = setup_logger('b92statistike-mongo2sql.log')
def workaround_freetds_bug(text):
"""
Emoticons in Instagram posts are outside of 0xffff unicode range
TDS doesn't like this. We need to use emoji package to convert
those pesky emoticons to text + there are some other emoticons
where emoji fails, I guess I should update emoji DB.
"""
text = emoji.demojize(text)
text = text.replace(u'🇫󾓮', u' ')
text = text.replace(u'🇺', u' ')
text = text.replace(u'🇺', u' ')
return text
def insert_one_news(news, cursor):
"""Insert one news from dictionary to SQL (jncluding all of its comments)"""
import logging
import tools
from gen.mvasmListener import *
logger = tools.setup_logger('sem_logger', 'sem.log', logging.DEBUG)
class Sem(mvasmListener):
def __init__(self, context):
self.MemPointer = 0
self.Sequence = []
self.Labels = {}
self.Context = context
def add(self, val):
self.Sequence.append(val)
def pop(self):
a = self.Sequence.pop()
return a
# Enter a parse tree produced by subleqasmParser#program.
def enterProgram(self, ctx: mvasmParser.ProgramContext):
logger.info("")
if self.Context['stage'] == 1:
self.MemPointer = 0
# Exit a parse tree produced by mvasmParser#program.
def exitProgram(self, ctx: mvasmParser.ProgramContext):
logger.info("")