Merge pull request #134 from rllm-team/develop

modify BRIDGE examples and rect model

examples/bridge/bridge_tacm12k.py

@@ -44,22 +44,20 @@
     _,
 ) = dataset.data_list
 
-# Making graph
-paper_embeddings = paper_embeddings.to(device)
 adj = build_homo_adj(
     relation_df=citations_table.df,
     n_all=len(papers_table),
 ).to(device)
 target_table = papers_table.to(device)
 y = papers_table.y.long().to(device)
+paper_embeddings = paper_embeddings.to(device)
 
 
 train_mask, val_mask, test_mask = (
     papers_table.train_mask,
     papers_table.val_mask,
     papers_table.test_mask,
 )
-out_dim = papers_table.num_classes
 
 
 class Bridge(torch.nn.Module):
@@ -79,6 +77,29 @@ def forward(self, table, non_table, adj):
         return node_feats[: len(table), :]
 
 
+t_encoder = TableEncoder(
+    in_dim=paper_embeddings.size(1),
+    out_dim=paper_embeddings.size(1),
+    table_transorm=FTTransformerTransform(col_stats_dict=papers_table.stats_dict),
+    table_conv=TabTransformerConv,
+)
+g_encoder = GraphEncoder(
+    in_dim=paper_embeddings.size(1),
+    out_dim=papers_table.num_classes,
+    graph_transform=GCNNorm(),
+    graph_conv=GCNConv,
+)
+model = Bridge(
+    table_encoder=t_encoder,
+    graph_encoder=g_encoder,
+).to(device)
+optimizer = torch.optim.Adam(
+    model.parameters(),
+    lr=args.lr,
+    weight_decay=args.wd,
+)
+
+
 def accuracy_score(preds, truth):
     return (preds == truth).sum(dim=0) / len(truth)
 
@@ -112,32 +133,8 @@ def test_epoch():
     return train_acc.item(), val_acc.item(), test_acc.item()
 
 
-t_encoder = TableEncoder(
-    in_dim=paper_embeddings.size(1),
-    out_dim=paper_embeddings.size(1),
-    table_transorm=FTTransformerTransform(col_stats_dict=papers_table.stats_dict),
-    table_conv=TabTransformerConv,
-)
-g_encoder = GraphEncoder(
-    in_dim=paper_embeddings.size(1),
-    out_dim=out_dim,
-    graph_transform=GCNNorm(),
-    graph_conv=GCNConv,
-)
-model = Bridge(
-    table_encoder=t_encoder,
-    graph_encoder=g_encoder,
-).to(device)
-
 start_time = time.time()
 best_val_acc = best_test_acc = 0
-optimizer = torch.optim.Adam(
-    [
-        dict(params=model.table_encoder.parameters(), lr=0.001),
-        dict(params=model.graph_encoder.parameters(), lr=0.01, weight_decay=1e-4),
-    ]
-)
-
 for epoch in range(1, args.epochs + 1):
     train_loss = train_epoch()
     train_acc, val_acc, test_acc = test_epoch()

examples/bridge/bridge_tlf2k.py

@@ -59,7 +59,6 @@
     artist_table.val_mask,
     artist_table.test_mask,
 )
-out_dim = artist_table.num_classes
 
 
 class Bridge(torch.nn.Module):
@@ -79,6 +78,29 @@ def forward(self, table, non_table, adj):
         return node_feats[: len(table), :]
 
 
+t_encoder = TableEncoder(
+    in_dim=emb_size,
+    out_dim=emb_size,
+    table_transorm=FTTransformerTransform(col_stats_dict=artist_table.stats_dict),
+    table_conv=TabTransformerConv,
+)
+g_encoder = GraphEncoder(
+    in_dim=emb_size,
+    out_dim=artist_table.num_classes,
+    graph_transform=GCNNorm(),
+    graph_conv=GCNConv,
+)
+model = Bridge(
+    table_encoder=t_encoder,
+    graph_encoder=g_encoder,
+).to(device)
+optimizer = torch.optim.Adam(
+    model.parameters(),
+    lr=args.lr,
+    weight_decay=args.wd,
+)
+
+
 def accuracy_score(preds, truth):
     return (preds == truth).sum(dim=0) / len(truth)
 
@@ -112,27 +134,8 @@ def test_epoch():
     return train_acc.item(), val_acc.item(), test_acc.item()
 
 
-t_encoder = TableEncoder(
-    in_dim=emb_size,
-    out_dim=emb_size,
-    table_transorm=FTTransformerTransform(col_stats_dict=artist_table.stats_dict),
-    table_conv=TabTransformerConv,
-)
-g_encoder = GraphEncoder(
-    in_dim=emb_size,
-    out_dim=out_dim,
-    graph_transform=GCNNorm(),
-    graph_conv=GCNConv,
-)
-model = Bridge(
-    table_encoder=t_encoder,
-    graph_encoder=g_encoder,
-).to(device)
-
-
 start_time = time.time()
 best_val_acc = best_test_acc = 0
-optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
 for epoch in range(1, args.epochs + 1):
     train_loss = train_epoch()
     train_acc, val_acc, test_acc = test_epoch()

examples/bridge/bridge_tml1m.py

@@ -63,7 +63,6 @@
     user_table.val_mask,
     user_table.test_mask,
 )
-out_dim = user_table.num_classes
 
 
 class Bridge(torch.nn.Module):
@@ -83,6 +82,29 @@ def forward(self, table, non_table, adj):
         return node_feats[: len(table), :]
 
 
+t_encoder = TableEncoder(
+    in_dim=emb_size,
+    out_dim=emb_size,
+    table_transorm=FTTransformerTransform(col_stats_dict=user_table.stats_dict),
+    table_conv=TabTransformerConv,
+)
+g_encoder = GraphEncoder(
+    in_dim=emb_size,
+    out_dim=user_table.num_classes,
+    graph_transform=GCNNorm(),
+    graph_conv=GCNConv,
+)
+model = Bridge(
+    table_encoder=t_encoder,
+    graph_encoder=g_encoder,
+).to(device)
+optimizer = torch.optim.Adam(
+    model.parameters(),
+    lr=args.lr,
+    weight_decay=args.wd,
+)
+
+
 def accuracy_score(preds, truth):
     return (preds == truth).sum(dim=0) / len(truth)
 
@@ -116,27 +138,8 @@ def test_epoch():
     return train_acc.item(), val_acc.item(), test_acc.item()
 
 
-t_encoder = TableEncoder(
-    in_dim=emb_size,
-    out_dim=emb_size,
-    table_transorm=FTTransformerTransform(col_stats_dict=user_table.stats_dict),
-    table_conv=TabTransformerConv,
-)
-g_encoder = GraphEncoder(
-    in_dim=emb_size,
-    out_dim=out_dim,
-    graph_transform=GCNNorm(),
-    graph_conv=GCNConv,
-)
-model = Bridge(
-    table_encoder=t_encoder,
-    graph_encoder=g_encoder,
-).to(device)
-
-
 start_time = time.time()
 best_val_acc = best_test_acc = 0
-optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
 for epoch in range(1, args.epochs + 1):
     train_loss = train_epoch()
     train_acc, val_acc, test_acc = test_epoch()

examples/ft_transformer.py

@@ -21,7 +21,7 @@
 sys.path.append("./")
 sys.path.append("../")
 from rllm.types import ColType
-from rllm.datasets.titanic import Titanic
+from rllm.datasets import Titanic
 from rllm.transforms.table_transforms import FTTransformerTransform
 from rllm.nn.conv.table_conv import FTTransformerConv
 

examples/rect.py

@@ -11,7 +11,7 @@
 
 # Datasets              Citeseer    |         Cora         | Pubmed
 # Unseen Classes  [1, 2, 5]  [3, 4] | [1, 2, 3]  [3, 4, 6] | [2]
-# RECT-L          66.50      68.40  | 74.80      72.20     | 75.30
+# RECT-L          61.10      66.10  | 71.20      70.40     | 69.90
 
 import argparse
 import copy
@@ -41,7 +41,7 @@
     [
         UT.NormalizeFeatures("l2"),
         UT.SVDFeatureReduction(200),
-        GT.GDC(),
+        GT.GCNNorm(),
     ]
 )
 

examples/tab_transformer.py

@@ -20,7 +20,7 @@
 sys.path.append("./")
 sys.path.append("../")
 from rllm.types import ColType
-from rllm.datasets.titanic import Titanic
+from rllm.datasets import Titanic
 from rllm.transforms.table_transforms import TabTransformerTransform
 from rllm.nn.conv.table_conv import TabTransformerConv
 

examples/tabnet.py

@@ -20,7 +20,7 @@
 sys.path.append("./")
 sys.path.append("../")
 from rllm.types import ColType
-from rllm.datasets.adult import Adult
+from rllm.datasets import Adult
 from rllm.transforms.table_transforms import TabNetTransform
 from rllm.nn.models import TabNet
 

examples/trompt.py

@@ -21,7 +21,7 @@
 sys.path.append("./")
 sys.path.append("../")
 from rllm.types import ColType
-from rllm.datasets.adult import Adult
+from rllm.datasets import Adult
 from rllm.transforms.table_transforms import TromptTransform
 from rllm.nn.conv.table_conv import TromptConv
 
@@ -43,7 +43,7 @@
 dataset = Adult(cached_dir=path)[0]
 dataset.to(device)
 
-# Split dataset, here the ratio of train-val-test is 80%-10%-10%
+# Split dataset, here the ratio of train-val-test is 26048-6513-16281
 train_loader, val_loader, test_loader = dataset.get_dataloader(
     26048, 6513, 16281, batch_size=args.batch_size
 )

rllm/datasets/__init__.py

@@ -1,4 +1,5 @@
 from .adult import Adult
+from .churn_modelling import ChurnModelling
 from .dblp import DBLP
 from .imdb import IMDB
 from .planetoid import PlanetoidDataset
@@ -11,6 +12,7 @@
 
 __all__ = [
     "Adult",
+    "ChurnModelling",
     "DBLP",
     "IMDB",
     "PlanetoidDataset",

rllm/datasets/adult.py

@@ -26,18 +26,18 @@ class Adult(Dataset):
     fnlwgt: The number of people the census believes have this job.
     Education: The highest level of education achieved (Bachelors,
         Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th,
-        7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.).
+        7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool).
     Education-Num: A numeric version of Education.
     Marital-Status: Marital status of the individual (Married-civ-spouse,
-        Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.).
+        Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse).
     Occupation: The kind of work individuals perform (Tech-support,
         Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty,
         Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing,
-        Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.).
+        Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces).
     Relationship: Relationship to head-of-household (Wife, Own-child, Husband,
-        Not-in-family, Other-relative, Unmarried.).
+        Not-in-family, Other-relative, Unmarried).
     Race: Race of the individual (White, Asian-Pac-Islander,
-        Amer-Indian-Eskimo, Other, Black.).
+        Amer-Indian-Eskimo, Other, Black).
     Sex: Gender of the individual.
     Capital-Gain: Total capital gains.
     Capital-Loss: Total capital losses.
@@ -53,8 +53,8 @@ class Adult(Dataset):
     .. parsed-literal::
 
         Statics:
-        Name   Passengers  Features
-        Size   48842         14
+        Name   Individuals  Features
+        Size   48842        14
 
     """