MetaLink code release

graphgym/config.py

@@ -94,6 +94,9 @@ def set_cfg(cfg):
     # Number of dataset splits: train/val/test
     cfg.share.num_splits = 1
 
+    # Number of task in targets. For multilabel tasks, this number > 1
+    cfg.share.num_task = 1
+
     # ----------------------------------------------------------------------- #
     # Dataset options
     # ----------------------------------------------------------------------- #

graphgym/contrib/__init__.py

@@ -6,9 +6,11 @@
 from .layer import *  # noqa
 from .loader import *  # noqa
 from .loss import *  # noqa
-from .network import *  # noqa
 from .optimizer import *  # noqa
 from .pooling import *  # noqa
 from .stage import *  # noqa
 from .train import *  # noqa
 from .transform import *  # noqa
+
+# import in the end
+from .network import *  # noqa

graphgym/contrib/config/metalink.py

@@ -0,0 +1,101 @@
+from yacs.config import CfgNode as CN
+
+from graphgym.register import register_config
+
+
+def set_cfg_example(cfg):
+    r'''
+    This function sets the default config value for customized options
+    :return: customized configuration use by the experiment.
+    '''
+
+    # ----------------------------------------------------------------------- #
+    # MetaLink KG options
+    # ----------------------------------------------------------------------- #
+
+    cfg.kg = CN()
+
+    cfg.kg.kg_mode = True
+
+    cfg.kg.dim_emb = 64
+
+    # type of prediction head: direct, mp, bilinear
+    cfg.kg.head = 'direct'
+
+    # how to decode pred
+    cfg.kg.decode = 'dot'
+
+    cfg.kg.layer_type = 'kgheteconv'
+
+    # normalize embedding
+    cfg.kg.norm_emb = False
+
+    # if do fine_tune after training
+    cfg.kg.fine_tune = False
+
+    # kg message passing layers
+    cfg.kg.layers_mp = 0
+
+    # kg aggregation
+    cfg.kg.agg = 'mean'
+
+    # kg message direction
+    cfg.kg.msg_direction = 'single'
+
+    # kg gate function
+    cfg.kg.gate_self = True
+    cfg.kg.gate_msg = True
+
+    # kg self transform
+    cfg.kg.self_trans = True
+
+    # Add self msg passing
+    cfg.kg.self_msg = 'none'
+
+    cfg.kg.has_act = True  # not sure
+
+    cfg.kg.has_bn = False  # picked
+
+    cfg.kg.hete = True  # picked
+
+    # last, every
+    cfg.kg.pred = 'every'  # picked
+
+    # no, every, last
+    cfg.kg.has_l2norm = 'no'  # picked, every if needed
+
+    # positioning l2 norm: pre, post
+    cfg.kg.pos_l2norm = 'post'
+
+    cfg.kg.gate_bias = False
+
+    # raw, loss, both
+    cfg.kg.edge_feature = 'raw'
+
+    # pertask, standard
+    cfg.kg.split = 'pertask'
+
+    # new, standard
+    cfg.kg.experiment = 'new'
+
+    # standard, relation
+    cfg.kg.setting = 'standard'
+
+    # Whether do meta inductive learning
+    cfg.kg.meta = False
+
+    # cfg.kg.meta_num = None
+
+    cfg.kg.meta_ratio = 0.2
+
+    # whether add aux target (logp, qed)
+    cfg.kg.aux = True
+
+    # keep what percentage of edges
+    cfg.kg.setting_ratio = 0.5
+
+    # number of repeats for evaluation
+    cfg.kg.repeat = 1
+
+
+register_config('metalink', set_cfg_example)