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algorithm_defaults = {
'ERM': {
'train_loader': 'standard',
'uniform_over_groups': False,
'eval_loader': 'standard',
},
'groupDRO': {
'train_loader': 'standard',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'group_dro_step_size': 0.01,
},
'deepCORAL': {
'train_loader': 'group',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'coral_penalty_weight': 1.,
},
'IRM': {
'train_loader': 'group',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'irm_lambda': 1.,
'irm_penalty_anneal_iters': 500,
},
'FLAG': {
'train_loader': 'standard',
'uniform_over_groups': False,
'eval_loader': 'standard',
'flag_step_size': 1e-2
},
'GCL': {
'train_loader': 'standard',
'uniform_over_groups': False,
'eval_loader': 'standard',
'use_cl': False,
'gcl_aug_prob': 0.5, #0.0 should be equiv to ERM, as aug is never applied, 1.0 is GCL setting where orig data is never seen
'aug_type': 'random', #'node_drop','edge_perm','random' can only use a single aug_type currently, or random choice of them
'gcl_aug_ratio': 0.1 # 0.0 should be equiv to ERM, as each graph is unchanged, 0.2 is GCL paper default
},
'GSN': {
'train_loader': 'standard',
'uniform_over_groups': False,
'eval_loader': 'standard',
},
'DANN': {
'train_loader': 'group',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'dann_lambda': 0.1
},
'CDANN': {
'train_loader': 'group',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'dann_lambda': 1.
},
'DANN-G': {
'train_loader': 'group',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'dann_lambda': 0.1
},
'MLDG': {
'train_loader': 'group',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'mldg_beta': 0.1
},
}
|