def read_pkls(hpconfigs=hpconfigs, name_pattern='hpconfig_(no_\w+)'):
root_dirs = {}
accuracies = {}
max_epoch_count = 0
min_epoch_count = 10000000
for hpconfig in hpconfigs:
try:
tasks = re.match(name_pattern, hpconfig)
if tasks:
tasks = tasks.group(1)
else:
tasks = 'main'
root_dirs[tasks] = initialize_task(hpconfig + '.py')
accuracies[tasks] = pickle.load(
open(
'{}/results/metrics/main.accuracy.pkl'.format(
root_dirs[tasks]), 'rb'))
if len(accuracies[tasks]) < min_epoch_count:
min_epoch_count = len(accuracies[tasks])
print('min_epoch_count: {}'.format(min_epoch_count))
if len(accuracies[tasks]) > max_epoch_count:
max_epoch_count = len(accuracies[tasks])
print('max_epoch_count: {}'.format(max_epoch_count))
except:
print('{} not found'.format(tasks))
return accuracies, min_epoch_count, max_epoch_count
def read_pkls():
accuracies = {}
max_epoch_count = 0
min_epoch_count = 10000000
hpconfig = 'hpconfig'
HPCONFIG = importlib.__import__(hpconfig)
tasks = '-'.join(str(i) for i in HPCONFIG.CONFIG.tasks)
root_dir= initialize_task(hpconfig + '.py')
print('root_dir: {}'.format(root_dir))
for filename in glob.glob('{}/results/metrics/*.accuracy.pkl'.format(root_dir)):
try:
task = os.path.basename(filename).split('.')[0]
accuracies[task] = pickle.load(
open(filename,
'rb')
)
if len(accuracies[task]) < min_epoch_count:
min_epoch_count = len(accuracies[task])
print('min_epoch_count: {}'.format(min_epoch_count))
if len(accuracies[task]) > max_epoch_count:
max_epoch_count = len(accuracies[task])
print('max_epoch_count: {}'.format(max_epoch_count))
except:
print('{} not found'.format(filename))
return accuracies, min_epoch_count, max_epoch_count
def read_pkls(hpconfigs=hpconfigs):
root_dirs = {}
accuracies = {}
max_epoch_count = 0
min_epoch_count = 10000000
for hpconfig in hpconfigs:
try:
HPCONFIG = importlib.__import__(hpconfig)
if len(HPCONFIG.CONFIG.tasks) > 1:
tasks = 'main'
else:
tasks = task_names[HPCONFIG.CONFIG.tasks[0]]
root_dirs[tasks] = initialize_task(hpconfig + '.py')
accuracies[tasks] = pickle.load(
open('{}/results/metrics/main.accuracy.pkl'.format(root_dirs[tasks]),
'rb')
)
if len(accuracies[tasks]) < min_epoch_count:
min_epoch_count = len(accuracies[tasks])
print('min_epoch_count: {}'.format(min_epoch_count))
if len(accuracies[tasks]) > max_epoch_count:
max_epoch_count = len(accuracies[tasks])
print('max_epoch_count: {}'.format(max_epoch_count))
except:
print('{} not found'.format(tasks))
return accuracies, min_epoch_count, max_epoch_count
def read_pkls(hpconfigs=hpconfigs):
root_dirs = defaultdict(list)
accuracies = defaultdict(list)
losses = defaultdict(list)
for hpconfig in hpconfigs:
try:
hpconfig = PREFIX + '.' + hpconfig
HPCONFIG = importlib.import_module(hpconfig)
model = re.search('.*hpconfig_(\w+)', hpconfig)
model = model.group(1)
log.info('hpconfig -- {}'.format(hpconfig))
for rd in run_dirs:
rd = initialize_task(hpconfig.replace('.', '/') + '.py', rd)
root_dirs[model].append(rd)
log.info(' rd: {}'.format(rd))
f = '{}/results/metrics/{}.test_accuracy.pkl'.format(
rd, HPCONFIG.CONFIG.model_name)
accuracies[model].append(pickle.load(open(f, 'rb')))
f = '{}/results/metrics/{}.test_loss.pkl'.format(
rd, HPCONFIG.CONFIG.model_name)
losses[model].append(pickle.load(open(f, 'rb')))
"""
if len(accuracies[model]) < EPOCH_COUNT_MIN:
EPOCH_COUNT_MIN = len(accuracies[model])
print('min_epoch_count: {}'.format( EPOCH_COUNT_MIN))
if len(accuracies[model]) > EPOCH_COUNT_MAX:
EPOCH_COUNT_MAX = len(accuracies[model])
print('max_epoch_count: {}'.format(EPOCH_COUNT_MAX))
"""
except:
log.exception('{} not found'.format(model))
return root_dirs, accuracies, losses, EPOCH_COUNT_MIN, EPOCH_COUNT_MAX
action='store_true',
dest='show_plot')
predict_parser.add_argument('--save-plot',
action='store_true',
dest='save_plot')
predict_parser.add_argument('--uniqueness', default=50, dest='beam_width')
args = parser.parse_args()
print(args)
if args.log_filter:
log.addFilter(CMDFilter(args.log_filter))
########################################################################################
# anikattu initialization for directory structure and so on
########################################################################################
ROOT_DIR = initialize_task(args.hpconfig, args.prefix_dir)
sys.path.append('.')
print(sys.path)
HPCONFIG = importlib.__import__(args.hpconfig.replace('.py', ''))
config.HPCONFIG = HPCONFIG.CONFIG
config.ROOT_DIR = ROOT_DIR
config.NAME = SELF_NAME
print('====================================')
print(ROOT_DIR)
print('====================================')
########################################################################################
# flush and load dataset or restore pickle file
########################################################################################
if config.CONFIG.flush:
from utilz import Sample, load_task_data
from utilz import PAD, word_tokenize
from utilz import VOCAB
from utilz import train, plot_attn, predict_batchop
from utilz import load_task1_data
from model import Net
SELF_NAME = os.path.basename(__file__).replace('.py', '')
import sys
import pickle
if __name__ == '__main__':
ROOT_DIR = initialize_task(SELF_NAME)
print('====================================')
print(ROOT_DIR)
print('====================================')
if config.CONFIG.flush:
log.info('flushing...')
dataset = load_task6_data()
pickle.dump(dataset, open('{}__cache.pkl'.format(SELF_NAME), 'wb'))
else:
dataset = pickle.load(open('{}__cache.pkl'.format(SELF_NAME), 'rb'))
log.info('dataset size: {}'.format(len(dataset.trainset)))
log.info('dataset[:10]: {}'.format(pformat(dataset.trainset[0])))
help='''starts a cli interface for running predictions
in inputs with best model from last training run'''
)
predict_parser.add_argument('--predict', default='predict', dest='task')
predict_parser.add_argument('--show-plot',
action='store_true',
dest='show_plot')
predict_parser.add_argument('--save-plot',
action='store_true',
dest='save_plot')
args = parser.parse_args()
print(args)
if args.log_filter:
log.addFilter(CMDFilter(args.log_filter))
ROOT_DIR = initialize_task(args.hpconfig)
sys.path.append('.')
print(sys.path)
HPCONFIG = importlib.__import__(args.hpconfig.replace('.py', ''))
config.HPCONFIG = HPCONFIG.CONFIG
config.ROOT_DIR = ROOT_DIR
config.NAME = SELF_NAME
print('====================================')
print(ROOT_DIR)
print('====================================')
if config.CONFIG.flush:
log.info('flushing...')
dataset = load_data(config, filename=config.HPCONFIG.dataset_path)
pickle.dump(dataset,
default='dump-cosine-similarity',
dest='task')
predict_parser = subparsers.add_parser('predict',
help='''starts a cli interface for running predictions
in inputs with best model from last training run''')
predict_parser.add_argument('--predict', default='predict', dest='task')
predict_parser.add_argument('--show-plot', action='store_true', dest='show_plot')
predict_parser.add_argument('--save-plot', action='store_true', dest='save_plot')
args = parser.parse_args()
print(args)
if args.log_filter:
log.addFilter(CMDFilter(args.log_filter))
ROOT_DIR = initialize_task(args.hpconfig, prefix=args.results_dir)
sys.path.append('.')
print(sys.path)
args.hpconfig = args.hpconfig.replace('/', '.').replace('.py', '')
modpath = args.hpconfig.split('.')
pkg, mod = '.'.join(modpath[:-1]), modpath[-1]
HPCONFIG = importlib.import_module(args.hpconfig)
config.HPCONFIG = HPCONFIG.CONFIG
config.ROOT_DIR = ROOT_DIR
config.NAME = SELF_NAME
print('====================================')
print(ROOT_DIR)
print('====================================')
action='store_true',
dest='show_plot')
predict_parser.add_argument('--save-plot',
action='store_true',
dest='save_plot')
args = parser.parse_args()
print(args)
if args.log_filter:
log.addFilter(CMDFilter(args.log_filter))
########################################################################################
# anikattu initialization for directory structure and so on
########################################################################################
ROOT_DIR = initialize_task(args.hpconfig,
args.prefix_dir,
base_hpconfig=args.base_hpconfig)
sys.path.append('.')
print(sys.path)
HPCONFIG = importlib.__import__(args.hpconfig.replace('.py', ''))
config.HPCONFIG = HPCONFIG.CONFIG
config.ROOT_DIR = ROOT_DIR
config.NAME = SELF_NAME
print('====================================')
print(ROOT_DIR)
print('====================================')
########################################################################################
# flush and load dataset or restore pickle file
########################################################################################
