def add_parser_arguments()

in agents/offline_agents.py [0:0]

• 71 lines of code
• 1 McCabe index (conditional complexity)

    def add_parser_arguments(cls, parser: 'argparse.ArgumentParser') -> None:
        parser = parser.add_argument_group('%s params' % cls.__name__)
        parser.add_argument('--dqn-train-batch-size', type=int, default=32)
        parser.add_argument('--dqn-updates', type=int, default=1000)
        parser.add_argument('--dqn-save-checkpoints-every',
                            type=int,
                            default=-1,
                            help='How often to save checkpoints')
        parser.add_argument(
            '--dqn-negative-sampling-prob',
            type=float,
            default=1.0,
            help='Relative probability to take negative example during'
            ' training. Value 1.0 means that the sampling is indenependent of'
            ' the label. Lower values will choose negative examples less'
            ' frequently.')
        parser.add_argument(
            '--dqn-balance-classes',
            type=int,
            default=0,
            help='Samples the same number of positives ane negatives for every'
            ' batch.')
        parser.add_argument('--dqn-network-type',
                            choices=('resnet18', 'simple'),
                            default='resnet18',
                            help='type of architecture to use')
        parser.add_argument(
            '--dqn-num-auccess-actions',
            type=int,
            default=0,
            help='If positive will run AUCCESS eval with this number of'
            ' actions.')
        parser.add_argument('--dqn-action-layers', type=int, default=1)
        parser.add_argument('--dqn-action-hidden-size', type=int, default=256)
        parser.add_argument('--dqn-eval-every',
                            type=int,
                            default=1000,
                            help='Eval every this many updates.')
        parser.add_argument('--dqn-cosine-scheduler',
                            type=int,
                            default=0,
                            help='Whether to use cosine scheduler.')
        parser.add_argument('--dqn-fusion-place',
                            choices=('first', 'last', 'all', 'none',
                                     'last_single'),
                            default='last')
        parser.add_argument('--dqn-learning-rate', type=float, default=3e-4)

        # Evaluation time paramters.
        parser.add_argument('--dqn-eval-batch-size', type=int, default=128)
        parser.add_argument(
            '--dqn-rank-size',
            type=int,
            default=-1,
            help='How many options to re-rank for eval. If negative, will use'
            ' train set.')
        parser.add_argument(
            '--dqn-load-from',
            help='If set, will skip the training and load the model from the'
            ' last checkpoint in the folder. Model architecture and training'
            ' params will be ignored.')
        parser.add_argument('--dqn-finetune-iterations',
                            type=int,
                            default=0,
                            help='If set, will fine-tune DQN on test data')
        parser.add_argument('--dqn-refine-iterations',
                            type=int,
                            default=0,
                            help='If set, will refine actions for each task')
        parser.add_argument('--dqn-refine-loss',
                            choices=('ce', 'linear'),
                            default='ce')
        parser.add_argument('--dqn-refine-lr', type=float, default=1e-4)