Detector Example

In this tutorial, you will learn the basic workflow of PyGOD with an example of DOMINANT. This tutorial assumes that you have basic familiarity with PyTorch and PyTorch Geometric (PyG).

(Time estimate: 5 minutes)

Data Loading

PyGOD use torch_geometric.data.Data to handle the data. Here, we use Cora, a PyG built-in dataset, as an example. To load your own dataset into PyGOD, you can refer to creating your own datasets tutorial in PyG.

import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid

data = Planetoid('./data/Cora', 'Cora', transform=T.NormalizeFeatures())[0]

Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph
Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index
Processing...
Done!

Because there is no ground truth label of outliers in Cora, we follow the method used by DOMINANT to inject 100 contextual outliers and 100 structure outliers into the graph. Note: If your dataset already contains the outliers you want to detect, you don’t have to inject more outliers.

import torch
from pygod.generator import gen_contextual_outlier, gen_structural_outlier

data, ya = gen_contextual_outlier(data, n=100, k=50)
data, ys = gen_structural_outlier(data, m=10, n=10)
data.y = torch.logical_or(ys, ya).long()

We also provide various type of built-in datasets. You can load them by passing the name of the dataset to load_data function. See data repository for more details.

from pygod.utils import load_data

data = load_data('inj_cora')
data.y = data.y.bool()

Initialization

You can use any detector by simply initializing without passing any arguments. Default hyperparameters are ready for you. Of course, you can also customize the parameters by passing arguments. Here, we use pygod.detector.DOMINANT as an example.

from pygod.detector import DOMINANT

detector = DOMINANT(hid_dim=64, num_layers=4, epoch=100)

Training

To train the detector with the loaded data, simply feed the torch_geometric.data.Data object into the detector via fit.

detector.fit(data)

DOMINANT(act=<function relu at 0x7fe4933e8220>,
         backbone=<class 'torch_geometric.nn.models.basic_gnn.GCN'>,
         batch_size=2708, compile_model=False, contamination=0.1,
         dropout=0.0, epoch=100, gpu=None, hid_dim=64, lr=0.004,
         num_layers=4, num_neigh=[-1, -1, -1, -1], save_emb=False,
         sigmoid_s=False, verbose=0, weight=0.5, weight_decay=0.0)

Inference

After training, the detector is ready to use. You can use the detector to predict the labels, raw outlier scores, probability of the outlierness, and prediction confidence. Here, we use the loaded data as an example.

pred, score, prob, conf = detector.predict(data,
                                           return_pred=True,
                                           return_score=True,
                                           return_prob=True,
                                           return_conf=True)
print('Labels:')
print(pred)

print('Raw scores:')
print(score)

print('Probability:')
print(prob)

print('Confidence:')
print(conf)

Labels:
tensor([0, 0, 0,  ..., 0, 0, 0])
Raw scores:
tensor([1.0254, 0.9655, 1.2170,  ..., 0.6176, 1.1248, 1.1259])
Probability:
tensor([0.0835, 0.0724, 0.1191,  ..., 0.0078, 0.1020, 0.1022])
Confidence:
tensor([1., 1., 1.,  ..., 1., 1., 1.])

Evaluation

To evaluate the performance outlier detector with AUC score, you can:

from pygod.metric import eval_roc_auc

auc_score = eval_roc_auc(data.y, score)
print('AUC Score:', auc_score)

AUC Score: 0.7676196920994756