def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
parser.add_argument('--surrogate-model',
default='RF',
choices=["RF", "ET", "GBRT", "DUMMY", "GP"],
help='type of surrogate model (learner)')
parser.add_argument('--liar-strategy',
default="cl_max",
choices=["cl_min", "cl_mean", "cl_max"],
help='Constant liar strategy')
parser.add_argument('--acq-func',
default="gp_hedge",
choices=["LCB", "EI", "PI", "gp_hedge"],
help='Acquisition function type')
parser.add_argument(
'--acq-kappa',
type=float,
default=1.96,
help=
'Controls how much of the variance in the predicted values should be taken into account. If set to be very high, then we are favouring exploration over exploitation and vice versa. Used when the acquisition is "LCB".'
)
parser.add_argument(
'--n-jobs',
default=-1,
type=int,
help='Number of processes to use for surrogate model (learner).')
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
parser.add_argument("--cliprange",
type=float,
default=0.2,
help="Clipping parameter of PPO.")
parser.add_argument(
"--ent-coef",
type=float,
default=0.01,
help=
"Entropy parameter for PPO. Adding entropy helps to avoid convergence to a local optimum. To increase the entropy parameter is to increase exploration."
)
parser.add_argument(
"--gamma",
type=float,
default=0.99,
help=
"Gamma parameter for advantage function in RL, discounting factor for rewards."
)
parser.add_argument(
"--lam",
type=float,
default=0.95,
help=
"Lambda parameter for advantage function in RL, advantage estimation discounting factor (lambda in the paper)."
)
parser.add_argument(
"--nminibatches",
type=int,
default=1,
help=
"Number of minibatches per environments. Here it's directly the number of batch of architectures."
)
parser.add_argument(
"--noptepochs",
type=int,
default=10,
help=
"Number of optimization steps to do per epochs. Basicaly it means the number of time you want to use learning data."
)
parser.add_argument('--max-evals',
type=int,
default=1e10,
help='maximum number of learning update.')
parser.add_argument('--network',
type=str,
default='ppo_lstm_128',
choices=[
'ppo_lstm_128', 'ppo_lnlstm_128',
'ppo_lstm_64', 'ppo_lnlstm_64', 'ppo_lstm_32',
'ppo_lnlstm_32'
],
help='Policy-Value network.')
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
parser.add_argument('--learner',
default='RF',
choices=["RF", "ET", "GBRT", "DUMMY", "GP"],
help='type of learner (surrogate model)')
parser.add_argument('--liar-strategy',
default="cl_max",
choices=["cl_min", "cl_mean", "cl_max"],
help='Constant liar strategy')
parser.add_argument('--acq-func',
default="gp_hedge",
choices=["LCB", "EI", "PI", "gp_hedge"],
help='Acquisition function type')
parser.add_argument('--n-jobs',
default=-1,
type=int,
help='Number of processes to use for learner.')
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
parser.add_argument(
"--surrogate-model",
default="RF",
choices=["RF", "ET", "GBRT", "DUMMY", "GP"],
help="Type of surrogate model (learner).",
)
parser.add_argument(
"--liar-strategy",
default="cl_max",
choices=["cl_min", "cl_mean", "cl_max"],
help="Constant liar strategy",
)
parser.add_argument(
"--acq-func",
default="gp_hedge",
choices=["LCB", "EI", "PI", "gp_hedge"],
help="Acquisition function type",
)
parser.add_argument(
"--kappa",
type=float,
default=1.96,
help='Controls how much of the variance in the predicted values should be taken into account. If set to be very high, then we are favouring exploration over exploitation and vice versa. Used when the acquisition is "LCB".',
)
parser.add_argument(
"--xi",
type=float,
default=0.01,
help='Controls how much improvement one wants over the previous best values. If set to be very high, then we are favouring exploration over exploitation and vice versa. Used when the acquisition is "EI", "PI".',
)
parser.add_argument(
"--n-jobs",
type=int,
default=1,
help="number of cores to use for the 'surrogate model' (learner), if n_jobs=-1 then it will use all cores available.",
)
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
add_arguments_from_signature(parser, AMBSMixed)
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
add_arguments_from_signature(parser, RegularizedEvolution)
return parser
def _extend_parser(parser):
NeuralArchitectureSearch._extend_parser(parser)
add_arguments_from_signature(parser, BoHpoNas)
return parser
