def __init__(self, app):
Config.__init__(self, app)
self.uni_file = os.path.join(self.settings_folder, 'me')
self.clientconfig_file = os.path.join(self.settings_folder, 'pypsyc', 'config')
#self.uni = self.get_uni()
import random
self.uni = 'psyc://beta.ve.symlynX.com/~pypsyctest' + ''.join([random.Random().choice('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ123456789') for i in [0]*10])
#self.uni = raw_input('uni?: ')
self.host = get_host(self.uni)
self.username = get_user(self.uni)
self.ui = 'wx'
def __init__(self, url):
self.config = Config()
# self.driver = webdriver.Chrome()
# self.driver = webdriver.Ie()
self.driver = webdriver.Firefox()
self.driver.get(url)
self.nowhandle = self.driver.current_window_handle
def AllowTransientNetworks(self, ipv4_list: List[str], ipv6_list: List[str]):
"""Temporarily allow connections from a network until the next reboot
(or when firewall rules are reloaded).
:param ipv4_list: List of IPv4 networks to allow
:type ipv4_list: list[str]
:param ipv6_list: List of IPv6 networks to allow
:type ipv6_list: list[str]
"""
parsed_ipv4 = Config.ParseNetworks(Config.IPv4, ipv4_list)
parsed_ipv6 = Config.ParseNetworks(Config.IPv6, ipv6_list)
if parsed_ipv4:
self.RunNFT(['add', 'element', *self.TRANSIENT_IPV4_SET, '{', ",".join(parsed_ipv4), '}'])
if parsed_ipv6:
self.RunNFT(['add', 'element', *self.TRANSIENT_IPV6_SET, '{', ",".join(parsed_ipv6), '}'])
pass
def __init__(self) -> None:
"""The Bot class, where the magic happens."""
intents = discord.Intents.all()
super().__init__(command_prefix='!',
case_insensitive=True,
intents=intents)
self.config = Config.load()
self.load_cogs()
self.run()
def __init__(self, app):
Config.__init__(self, app)
self.serverconfig_file = os.path.join(self.settings_folder, 'config')
import os
import sys
import xgbfir
import pandas as pd
import xgboost as xgb
sys.path.append("../src")
from base import Utilities, Config
from common import CustomTransformation
config = Config()
train_module = CustomTransformation(config, 'train')
watchlist = [(train_module.ddata, 'train')]
print(train_module.final_columns)
params = Utilities.load_json(config.params_file)
history = xgb.cv(params, train_module.ddata, 300, early_stopping_rounds=30, metrics=["auc", "error"], verbose_eval=True)
model = xgb.train(params, train_module.ddata, 200, verbose_eval=True)
class_mapping = Utilities.load_json(config.class_mapping_file)
test_module = CustomTransformation("test", class_mapping, train_module.final_columns)
y_pred = model.predict(test_module.ddata)
submission_df = pd.DataFrame({config.notable_columns["ID"]: list(test_module.main_column.values),
config.notable_columns["Target"]: list(y_pred)})
submission_df.to_csv(os.path.join(config.home, 'submission', 'one.csv'), float_format='%0.6f', index=False)
import argparse
from base import Config, train
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--lr', default=0.01, type=float)
parser.add_argument('--ld', default=0.3, type=float)
parser.add_argument('--wd', default=0.001, type=float)
parser.add_argument('--dr', default=1, type=int)
parser.add_argument('--ar', default='peng', type=str)
parser.add_argument('--ch', nargs='+', default=(32, 64, 128, 256, 256, 64), type=int)
parser.add_argument('--ta', default='ageC', type=str)
args = parser.parse_args()
cfg = Config(learning_rate=args.lr, lr_decay=args.ld, weight_decay=args.wd, dropout=bool(args.dr), arch=args.ar,
channels=args.ch, target=args.ta)
train(cfg)
warnings.warn = warn
if __name__ == '__main__':
rep = int(os.environ['SLURM_ARRAY_TASK_ID'])
parser = argparse.ArgumentParser()
parser.add_argument('--tss',
default=100,
type=int,
help='training sample size')
parser.add_argument(
'--ssd',
default='/SampleSplits/',
help='sample splits directory (default: /SampleSplits/)')
parser.add_argument('--nw', default=8, type=int, help='number of workers')
parser.add_argument('--bs', default=16, type=int, help='batch size#')
parser.add_argument('--sn', default='age', type=str, help='scorename')
parser.add_argument('--lr', default=0.01, type=float)
args = parser.parse_args()
cfg = Config(sample_size=args.tss,
repetition_num=rep,
sample_splits_dir=args.ssd,
num_workers=args.nw,
batch_size=args.bs,
scorename=args.sn,
learning_rate=args.lr)
train(cfg)