Add files via upload

.gitignore

+ModeConvModel/results
+processed_lux
+processed_simulated_smart_bridge
+lightning_logs
+
+*.ckpt
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README.md

-# ModeConv
-Official repository to the paper "ModeConv: A novel convolution for distinguishing anomalous and normal structural behavior"
+# ModeConv: A novel convolution for distinguishing anomalous and normal Structural Behavior
+
+
+
+## Installation
+
+Dependencies:
+
+```
+pip install torch --index-url https://download.pytorch.org/whl/cu117
+pip install -r requirements.txt
+```
+
+## How to use
+
+### Training and evaluating a model
+```
+python train.py
+
+optional arguments:
+  -h, --help           show this help message and exit
+  --model MODEL        options: 'ModeConvFast', 'ModeConvLaplace', 'ChebConv', 'AGCRN', 'MtGNN' (default: 'ModeConvFast')
+  --dataset DATASET    options: 'simulated_smart_bridge', 'luxemburg' (default: 'luxemburg')
+  --epochs N           (default: 50)
+  --batch-size N       (default: 256)
+  --lr lr              initial learning rate for optimizer e.g.: 1e-4 | 'auto' (default: 'auto')
+  --num-layer N        (default: 3)
+  --decoder DECODER    options: 'linear' for linear layer only decoder, 'custom': to use ModeConv/ChebConv/etc. layers in decoder (default: 'custom')
+  --hidden-dim N       (default: 8)
+  --bottleneck N       (default: 2)
+  --no-maha-threshold  mahalanobis threshold calculation/evaluation is very slow; disabling saves 30min-2h in val and test on luxemburg dataset (default: True)
+  --seed N             (default: 3407)
+  --no-cuda            (default: False)
+
+```
+
+### Evaluating a model
+```
+python eval.py Path
+
+positional arguments:
+  Path                 path to pretrained weights, e.g. data/pretrained/luxemburg/ModeConvFast/GNN.statedict
+
+optional arguments:
+  -h, --help           show this help message and exit
+  --model MODEL        options: 'ModeConvFast', 'ModeConvLaplace', 'ChebConv', 'AGCRN', 'MtGNN' (default: 'ModeConvFast')
+  --dataset DATASET    options: 'simulated_smart_bridge', 'luxemburg' (default: 'luxemburg')
+  --batch-size N       (default: 256)
+  --num-layer N        (default: 3)
+  --decoder DECODER    options: 'linear' for linear layer only decoder, 'custom': to use ModeConv/ChebConv/etc. layers in decoder (default: 'custom')
+  --hidden-dim N       (default: 8)
+  --bottleneck N       (default: 2)
+  --no-maha-threshold  mahalanobis threshold calculation/evaluation is very slow; disabling saves 30min-2h in val and test on luxemburg dataset (default: True)
+  --seed N             (default: 3407)
+  --no-cuda            (default: False)
+
+```
+
+## Datasets
+
+The csv files for the luxemburg dataset only contain about 5% of the full data used.
+
+The csv files for the simulated_smart_bridge dataset only contain about 6% of the full data.
+
+## Results
+
+### Luxemburg
+
+```
+python train.py --model {Model} --dataset luxemburg
+```
+
+| Model            | AUC   | F1    |
+|----------------- |-------|-------|
+| ModeConvFast     | 99.99 | 93.09 |
+| ModeConvLaplace  | 92.16 | 73.74 |
+| ChebConv         | 92.07 | 74.53 |
+| AGCRN            | 98.18 | 86.67 |
+| MtGNN            | 99.99 | 82.00 |
+
+### Simulated Smart Bridge
+
+```
+python train.py --model {Model} --dataset simulated_smart_bridge
+```
+
+| Model            | AUC   | F1    |
+|----------------- |-------|-------|
+| ModeConvFast     | 92.23 | 87.93 |
+| ModeConvLaplace  | 92.43 | 88.07 |
+| ChebConv         | 82.15 | 83.89 |
+| AGCRN            | 92.26 | 87.76 |
+| MtGNN            | 91.19 | 86.78 |
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eval.py

+import datetime
+import json
+import sys
+import os
+import torch
+import torch_geometric as pyg
+import pytorch_lightning as pl
+from torch_geometric.loader import DataLoader
+
+from ModeConvModel.models.select_model import select_model
+from ModeConvModel.dataset import SimulatedSmartBridgeDataset, LuxemburgDataset
+import argparse
+
+
+def getArgs(argv=None):
+    parser = argparse.ArgumentParser(description="ModeConv")
+    parser.add_argument("weights", metavar="Path",
+                        help="path to pretrained weights, e.g. data/pretrained/luxemburg/ModeConvFast/GNN.statedict")
+    parser.add_argument("--model", default="ModeConvFast", help="options: 'ModeConvFast', 'ModeConvLaplace', 'ChebConv', 'AGCRN', 'MtGNN'")
+    parser.add_argument("--dataset", default="luxemburg", help="options: 'simulated_smart_bridge', 'luxemburg'")
+    parser.add_argument('--batch-size', type=int, default=256, metavar="N")
+    parser.add_argument('--num-layer', type=int, default=3, metavar="N")
+    parser.add_argument('--decoder', default="custom", help="options: 'linear' for linear layer decoder;"
+                                                                     "'custom': to use ModeConv/ChebConv/etc. layers in decoder")
+    parser.add_argument('--hidden-dim', type=int, default=8, metavar="N")
+    parser.add_argument('--bottleneck', type=int, default=2, metavar="N")
+    parser.add_argument("--no-maha-threshold", action="store_true", default=True,
+                        help="mahalanobis threshold calculation/evaluation is very slow; disabling saves 30min+ in val and test on luxemburg dataset")
+    parser.add_argument('--seed', type=int, default=3407, metavar="N")
+    parser.add_argument("--no-cuda", action="store_true", default=False)
+
+    args = parser.parse_args(argv)
+    args.__dict__["lr"] = 0
+
+    return args
+
+
+if __name__ == "__main__":
+    args = getArgs(sys.argv[1:])
+    pl.seed_everything(args.seed)
+
+    starttime = datetime.datetime.now()
+    starttime = starttime.strftime("%H:%M:%S")
+
+    model = select_model(args)
+    model.load_state_dict(torch.load(args.weights))
+
+    trainer = pl.Trainer(
+        logger=True,
+        enable_checkpointing=False,
+        max_epochs=0,
+        gpus=0 if args.no_cuda else 1,
+    )
+
+    if args.dataset == "simulated_smart_bridge":
+        val_ds = SimulatedSmartBridgeDataset("./processed_simulated_smart_bridge/", mode="val")
+    elif args.dataset == "luxemburg":
+        val_ds = LuxemburgDataset("./processed_lux", mode="val")
+    val_dl = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)
+    trainer.validate(model, val_dl)
+    del val_ds, val_dl
+
+    if args.dataset == "simulated_smart_bridge":
+        test_ds = SimulatedSmartBridgeDataset("./processed_simulated_smart_bridge/", mode="test")
+    elif args.dataset == "luxemburg":
+        test_ds = LuxemburgDataset("./processed_lux", mode="test")
+
+    test_dl = DataLoader(test_ds, batch_size=args.batch_size, shuffle=False)
+    trainer.test(model, test_dl)
+
+    endtime = datetime.datetime.now()
+    endtime = endtime.strftime("%H:%M:%S")
+    out = {"args": vars(args), "Start time": starttime, "End time": endtime,
+           "Last epoch duration": model.epoch_duration}
+    with open(model.prefix + "/args.json", "w") as outfile:
+        json.dump(out, outfile, indent=4, sort_keys=False)
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requirements.txt

+torch-geometric==2.2.0
+torch-cluster -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
+torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
+torch-sparse -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
+torch-spline-conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
+numpy==1.23.5
+scipy==1.10.1
+scikit-learn==1.2.1
+pandas==1.5.3
+networkx==3.0
+tqdm==4.64.1
+pytorch-lightning==1.9.2
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train.py

+import datetime
+import json
+import sys
+import os
+import torch
+import torch_geometric as pyg
+import pytorch_lightning as pl
+from torch_geometric.loader import DataLoader
+
+from ModeConvModel.models.select_model import select_model
+from ModeConvModel.dataset import SimulatedSmartBridgeDataset, LuxemburgDataset
+import argparse
+
+
+def train(data_module, args):
+    starttime = datetime.datetime.now()
+    starttime = starttime.strftime("%H:%M:%S")
+
+    model = select_model(args)
+    auto_lr = True if args.lr == "auto" else False
+
+    trainer = pl.Trainer(
+        logger=True,
+        enable_checkpointing=False,
+        max_epochs=args.epochs,
+        gpus=0 if args.no_cuda else 1,
+        auto_lr_find=auto_lr                         # run learning rate finder, results override hparams.learning_rate
+    )
+
+    # call tune to find the batch_size and to optimize lr
+    trainer.tune(model, data_module)
+    data_module.kwargs["batch_size"] = model.batch_size
+    trainer.fit(model, data_module)
+    torch.save(model.state_dict(), model.prefix + "/GNN.statedict")
+    del data_module
+
+    trainendtime = datetime.datetime.now()
+    trainendtime = trainendtime.strftime("%H:%M:%S")
+    print("Current Time =", trainendtime)
+    print()
+
+    if args.dataset == "simulated_smart_bridge":
+        val_ds = SimulatedSmartBridgeDataset("./processed_simulated_smart_bridge/", mode="val")
+    elif args.dataset == "luxemburg":
+        val_ds = LuxemburgDataset("./processed_lux", mode="val")
+    val_dl = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)
+    trainer.validate(model, val_dl)
+    del val_ds, val_dl
+
+    if args.dataset == "simulated_smart_bridge":
+        test_ds = SimulatedSmartBridgeDataset("./processed_simulated_smart_bridge/", mode="test")
+    elif args.dataset == "luxemburg":
+        test_ds = LuxemburgDataset("./processed_lux", mode="test")
+
+    test_dl = DataLoader(test_ds, batch_size=args.batch_size, shuffle=False)
+    trainer.test(model, test_dl)
+
+    endtime = datetime.datetime.now()
+    endtime = endtime.strftime("%H:%M:%S")
+    out = {"args": vars(args), "Start time": starttime, "Train end time": trainendtime, "End time": endtime,
+           "Last epoch duration": model.epoch_duration}
+    with open(model.prefix + "/args.json", "w") as outfile:
+        json.dump(out, outfile, indent=4, sort_keys=False)
+
+
+def getArgs(argv=None):
+    parser = argparse.ArgumentParser(description="ModeConv")
+    parser.add_argument("--model", default="ModeConvFast", help="options: 'ModeConvFast', 'ModeConvLaplace', 'ChebConv', 'AGCRN', 'MtGNN'")
+    parser.add_argument("--dataset", default="luxemburg", help="options: 'simulated_smart_bridge', 'luxemburg'")
+    parser.add_argument("--epochs", type=int, default=50, metavar="N")
+    parser.add_argument('--batch-size', type=int, default=256, metavar="N")
+    parser.add_argument("--lr", default="auto", metavar="lr",  # 1e-4
+                        help="initial learning rate for optimizer e.g.: 1e-4 | 'auto'")
+    parser.add_argument('--num-layer', type=int, default=3, metavar="N")
+    parser.add_argument('--decoder', default="custom", help="options: 'linear' for linear layer decoder;"
+                                                                     "'custom': to use ModeConv/ChebConv/etc. layers in decoder")
+    parser.add_argument('--hidden-dim', type=int, default=8, metavar="N")
+    parser.add_argument('--bottleneck', type=int, default=2, metavar="N")
+    parser.add_argument("--no-maha-threshold", action="store_true", default=True,
+                        help="mahalanobis threshold calculation/evaluation is very slow; disabling saves 30min+ in val and test on luxemburg dataset")
+    parser.add_argument('--seed', type=int, default=3407, metavar="N")
+    parser.add_argument("--no-cuda", action="store_true", default=False)
+
+    args = parser.parse_args(argv)
+
+    return args
+
+
+if __name__ == "__main__":
+    args = getArgs(sys.argv[1:])
+
+    pl.seed_everything(args.seed)
+    if args.dataset == "simulated_smart_bridge":
+        train_ds = SimulatedSmartBridgeDataset("./processed_simulated_smart_bridge/", mode="train")
+    elif args.dataset == "luxemburg":
+        train_ds = LuxemburgDataset("./processed_lux", mode="train")
+    else:
+        raise NotImplementedError(f"Dataset {args.dataset} not found; Choices: simulated_smart_bridge, luxemburg")
+
+    data_module = pyg.data.LightningDataset(
+        train_dataset=train_ds,
+        # val_dataset=val_ds,
+        # test_dataset=test_ds,
+        batch_size=args.batch_size,
+        num_workers=0
+    )
+
+    train(data_module=data_module, args=args)