def __init__(self):
# Parse initial experiment arguments
initial_arguments = ExperimentArguments(sections=('experiment', ),
use_all_cli_args=False)
initial_arguments.add_class_arguments(Experiment)
initial_arguments.get_arguments()
self.is_master = initial_arguments.is_master
self.initialize_backend(initial_arguments.backend.title())
self.ModelClass = getattr(model_module,
initial_arguments.model_class_name)
self.ReaderClass = getattr(reader_module,
initial_arguments.reader_class_name)
self.BudgetDecoderClass = getattr(
src.hpbandster.budget_decoder,
initial_arguments.budget_decoder_class_name)
self.TrainerClass = TrainerClass
# Populate experiment arguments with arguments from specific classes
self.experiment_arguments = ExperimentArguments(use_all_cli_args=True)
self.experiment_arguments.add_class_arguments(Experiment)
self.experiment_arguments.add_class_arguments(self.ModelClass)
self.experiment_arguments.add_class_arguments(self.ReaderClass)
self.experiment_arguments.add_class_arguments(self.TrainerClass)
self.experiment_arguments.add_class_arguments(TrainManager)
self.experiment_arguments.add_class_arguments(BayesianOptimizer)
self.experiment_arguments.add_class_arguments(ConfigGenerator)
self.experiment_arguments.get_arguments()
verbose = initial_arguments.verbose
setup_logging(self.experiment_arguments.working_dir,
logging.DEBUG if verbose else logging.INFO)
self.train_manager = TrainManager(
ModelClass=self.ModelClass,
ReaderClass=self.ReaderClass,
TrainerClass=self.TrainerClass,
**self.experiment_arguments.get_arguments())
self.budget_decoder = self.BudgetDecoderClass(
**self.experiment_arguments.get_arguments())
class Experiment:
"""
Current design:
User provides the names of the classes that should be used for reading the data, decoding the hyperband budget,
etc.. This way each experiment can be saved and restored just based on the .ini file that is saved together
with the logs.
Drawback:
Hard to use it as an external library, since new readers, models etc. have to be placed in a correct directory.
Solution:
We could add modules from which imports are made as an arguments to this class constructor.
We leave it like this for now.
"""
@staticmethod
# Parsing arguments
def add_arguments(parser):
parser.section('experiment')
parser.add_argument(
"working_dir",
type=str,
help="Directory for results and other important stuff.")
parser.add_argument("model_class_name",
default='SimpleRNN',
help="Model class used for the training.")
parser.add_argument("reader_class_name",
default='AnomalyDataReader',
help="Reader class used for the training.")
parser.add_argument(
"budget_decoder_class_name",
default='SimpleBudgetDecoder',
help="Class used to update setting based on higher budget.")
parser.add_argument("backend",
default="Pytorch",
help="Whether to use Tensorflow or Pytorch.")
parser.add_argument("verbose",
type=int,
default=0,
choices=[0, 1],
help="If set to 1 then log debug messages.")
parser.add_argument(
"is_master",
type=int,
default=0,
choices=[0, 1],
help="If set to 1 then it will run thread for BO optimization.")
return parser
def __init__(self):
# Parse initial experiment arguments
initial_arguments = ExperimentArguments(sections=('experiment', ),
use_all_cli_args=False)
initial_arguments.add_class_arguments(Experiment)
initial_arguments.get_arguments()
self.is_master = initial_arguments.is_master
self.initialize_backend(initial_arguments.backend.title())
self.ModelClass = getattr(model_module,
initial_arguments.model_class_name)
self.ReaderClass = getattr(reader_module,
initial_arguments.reader_class_name)
self.BudgetDecoderClass = getattr(
src.hpbandster.budget_decoder,
initial_arguments.budget_decoder_class_name)
self.TrainerClass = TrainerClass
# Populate experiment arguments with arguments from specific classes
self.experiment_arguments = ExperimentArguments(use_all_cli_args=True)
self.experiment_arguments.add_class_arguments(Experiment)
self.experiment_arguments.add_class_arguments(self.ModelClass)
self.experiment_arguments.add_class_arguments(self.ReaderClass)
self.experiment_arguments.add_class_arguments(self.TrainerClass)
self.experiment_arguments.add_class_arguments(TrainManager)
self.experiment_arguments.add_class_arguments(BayesianOptimizer)
self.experiment_arguments.add_class_arguments(ConfigGenerator)
self.experiment_arguments.get_arguments()
verbose = initial_arguments.verbose
setup_logging(self.experiment_arguments.working_dir,
logging.DEBUG if verbose else logging.INFO)
self.train_manager = TrainManager(
ModelClass=self.ModelClass,
ReaderClass=self.ReaderClass,
TrainerClass=self.TrainerClass,
**self.experiment_arguments.get_arguments())
self.budget_decoder = self.BudgetDecoderClass(
**self.experiment_arguments.get_arguments())
# We might implement in the future backends for different libraries, for example Tensorflow
@staticmethod
def initialize_backend(backend):
assert backend == 'Pytorch', 'Currently only Pytorch backend is implemented'