def change():
if not request.args or 'value' not in request.args:
abort(400, 'Missing request arguments.')
if 'date' in request.args:
date = parse_date(request.args['date'])
else:
date = controller.newest_date()
data = controller.timeline(request.args['value'],
start_date=date - timedelta(days=1),
end_date=date)
day_before, day_of = data
result = {
stat_type: {
'change':
day_of[stat_type] - day_before[stat_type],
'changePercent': (day_of[stat_type] - day_before[stat_type]) /
day_before[stat_type] *
100 if day_before[stat_type] > 0 else 0,
'dayOf':
day_of[stat_type],
'dayBefore':
day_before[stat_type]
}
for stat_type in ['confirmed', 'deaths', 'recovered', 'active']
}
return jsonify({'data': result})
def prepare_eruption_params(eruption_tuple):
eruption_params = {
'startDate':
parse_date(
datetime.datetime(int(eruption_tuple[1]), int(eruption_tuple[2]),
int(eruption_tuple[3]))).isoformat(),
'completionDate':
add_seven_days(
parse_date(
datetime.datetime(int(eruption_tuple[1]),
int(eruption_tuple[2]),
int(eruption_tuple[3])))).isoformat(),
'lat':
eruption_tuple[4],
'lon':
eruption_tuple[5]
}
return eruption_params
def timeline():
args = request.args
if not args or not 'value' in args:
abort(400, "Missing request arguments")
if "start_date" in args:
start_date = parse_date(args["start_date"])
if start_date < FIRST_DATE:
abort(422, "Date out of range.")
else:
start_date = FIRST_DATE
if "end_date" in args:
end_date = parse_date(args["end_date"])
else:
end_date = datetime.today()
data = controller.timeline(args['value'],
start_date=start_date,
end_date=end_date)
return jsonify({'data': data})
def parse_args():
parser = ArgParser(default_config_files=[os.getcwd() + '/src/initial_configurations/default'])
# Core setting
core_parse = parser.add_argument_group('Core setting')
core_parse.add_argument('-s', '--start_date', dest='start_date', default='now', type=str, help='Training start date')
core_parse.add_argument('-p', '--train_period', dest='train_period', default=-1, type=int, help='Time period of training file is used')
core_parse.add_argument('-n', '--new_run', dest='new_run', default=0, type=int, help='If the model checkpoint is erased to run new model')
core_parse.add_argument('-l', '--local_run', dest='local_run', default=0, type=int, help='If the parameter JSON file is kept locally insteat to redis')
core_parse.add_argument('-nni', '--tuning', dest='tuning', default=0, type=int, help='Whether or not to peform NNI hyper parameter tuning')
# Model
model_parse = parser.add_argument_group('Model')
model_parse.add_argument('-m', '--model', dest='model', default='DNN', type=str, help='Select the model to train e.g. DNN')
model_parse.add_argument('--loss', dest='loss', default=30, type=int, help="Setting of loss function '10','11','12','20','21','22','30','31','32'" )
model_parse.add_argument('--hidden_units', dest='hidden_units', default=[128, 64], type=int, nargs='+', help='List containing the number of hidden units to use for each hidden layer')
model_parse.add_argument('--dropout_rate', dest='dropout_rate', default=0.5, type=float, help='List containing the number of dropout rate to use for each hidden layer')
model_parse.add_argument('--one_hot_units', dest='one_hot_units', default=[2, 35, 359, 3, 2], type=int, nargs='+', help='List containing the number of embedding units to use for features (in order): [weekday, region, city, adexchange, slotformat]; this replaces the one hot encoding')
model_parse.add_argument('--multi_hot_units', dest='multi_hot_units', default=[45], type=int, nargs='+', help='List containing the number of embedding units to use for features: [usertag]')
model_parse.add_argument('--learning_rate', dest='learning_rate', default=0.002, type=float, help='Learning rate of updating gradient')
model_parse.add_argument('--decay_step', dest='decay_step', default=100, type=int, help='Decay step')
model_parse.add_argument('--decay_rate', dest='decay_rate', default=0.98, type=float, help='Decay rate for exponential decay of learning rate')
model_parse.add_argument('--class_ratio', dest='class_ratio', default=0.5, type=float, help='Ratio of 2 classes for imbalanced data')
model_parse.add_argument('--alpha', dest='alpha', default=1., type=float, help='Alpha for Focal loss regularization in DNN')
model_parse.add_argument('--beta', dest='beta', default=1., type=float, help='Beta for regularization')
model_parse.add_argument('--gamma', dest='gamma', default=1., type=float, help='Gamma for Focal loss regularization in DNN')
# Training
train_parse = parser.add_argument_group('Training hyperparameters')
train_parse.add_argument('--save_summary_steps', dest='save_summary_steps', default=100, type=int, help='save summary steps')
train_parse.add_argument('--log_step_count_steps', dest='log_step_count_steps', default=100, type=int, help='logging step count steps')
train_parse.add_argument('--checkpoints_steps', dest='save_checkpoints_steps',default=500, type=int, help='checkpoints steps')
train_parse.add_argument('--has_gpu', dest='has_gpu', default=0, type=int, help='1 if GPU is present, else 0')
train_parse.add_argument('--oversample', dest='oversample', default=0, type=int, help='1 if will oversample training dataset, else 0')
train_parse.add_argument('--is_test', dest='is_test', default=0, type=int, help='1 if the trained model will be evaluated, else 0')
train_parse.add_argument('--num_epochs', dest='num_epochs', default=1.0, type=float, help='Number of total epochs')
train_parse.add_argument('--start_delay_secs', dest='start_delay_secs', default=10, type=int, help='Start evaluating after 10 secs')
train_parse.add_argument('--throttle_secs', dest='throttle_secs', default=10, type=int, help='Evaluate only every 30 secs')
train_parse.add_argument('--batch_size', dest='batch_size', default=128, type=int, help='Number of examples per batch')
# Directory paths
dir_parse = parser.add_argument_group('Directory paths')
dir_parse.add_argument('--train_data_path', dest='train_data_path', default='./data/', type=str, help='Directory where the training files are located')
dir_parse.add_argument('--save_dir', dest='save_dir', default='./Outputs/', type=str, help='Directory to save model directories')
dir_parse.add_argument('--load_dir', dest='load_dir', default='latest', type=str, help='Directory to load old model,default "new" as the latest model')
dir_parse.add_argument('--store_dir', dest='store_dir', default='latest', type=str, help='Directory to store current model, default "latest" to save in timestamp')
dir_parse.add_argument('--builder_save_dir', dest='builder_save_dir', default='builder_save', type=str, help='Directory to store current model for tfjs predictor')
_args, _ = parser.parse_known_args()
_params = vars(_args)
_params['train_data_path'] = os.getcwd() + _params['train_data_path']
# Identify whether it's using NNI tuning mode
if _params['tuning'] == 1:
import nni
tuner_params = nni.get_next_parameter()
try:
_params.update(tuner_params)
except Exception as err:
tf.logging.error('Error args updated: %s', err)
tf.logging.error('Failed with params: %s', str(_params))
_params['num_features'] = len(INT_NUM_FEAT) + sum(_params['one_hot_units']) + sum(_params['multi_hot_units'])
_params['model_name'] = _params['model']
# Adjust filename to restore/save by config settings
if _params['store_dir'] == 'latest':
_params['store_dir'] = _params['model_name'] + '_' + parse_date('now').strftime(FILE_DATE_FORMAT)
if _params['load_dir'] == 'latest':
_params['load_dir'] = find_latest_model_dir(_params['save_dir'], _params['store_dir'], _params['model_name'])
if _params['new_run'] == 1:
_params['load_dir'] = _params['store_dir']
return _params
has_gpu=train_hps.has_gpu,
data_file=tfrecordfiles,
is_test=train_hps.is_test,
to_ohe=args['to_ohe'],
random_seed=args['random_seed'],
num_cores=args['num_cores'],
train_ratio=args['train_ratio'],
valid_ratio=args['valid_ratio'],
batch_size=args['batch_size'],
prefetch_size=args['prefetch_size'])
# Set model directory hyperparameters
args['model_name'] = args['model']
if args['store_dir'] == 'latest':
store_dir = args['model_name'] + '_' + \
parse_date('now').strftime("%Y-%m-%dT%H:%M:%S")
else:
store_dir = args['store_dir']
if args['load_dir'] == 'latest': # and new_run == 0
load_dir = find_latest_model_dir(
args['save_dir'], store_dir, args['model_name'])
else:
load_dir = args['load_dir']
if args['new_run'] == 1:
load_dir = store_dir
dir_hps = tf.contrib.training.HParams(
save_dir=args['save_dir'],
load_dir=load_dir,