dgld.utils.dataset

This is a program about loading data.

class dgld.utils.dataset.GraphNodeAnomalyDectionDataset(name='Cora', raw_dir=None, p=15, k=50, is_preprocess_features=True, g_data=None, y_data=None)[source]

Bases: DGLDataset

This is a class to get graph and inject anomaly follow [CoLA](https://arxiv.org/abs/2103.00113),inject Anomaly to the oral graph We fix p = 15 and set q to 10, 15, 20, 5, 5, 20, 200 for BlogCatalog, Flickr, ACM, Cora, Citeseer, Pubmed and ogbn-arxiv, respectively.

Parameters

name (str) – when name == ‘custom’, using custom data and please Specify custom data by g_data. and Specify label by y_data. [BlogCatalog, Flickr, Cora, Citeseer, Pubmed and ogbn-arxiv] is supported default follow CoLA.
raw_dir (str) – Save data path. Supports user customization.
p (int) – anomaly injection hyperparameter follow CoLA, for structural anomaly
k (int) – anomaly injection hyperparameter follow CoLA, for contextual anomaly
is_preprocess_features (bool) – follow the same preprocess as CoLA, default by True
g_data (DGL.Graph) – Specify custom data by g_data.
y_data (Torch.Tensor) – Specify custom label by g_data.

Examples

>>> import GraphNodeAnomalyDectionDataset
>>> dataset = GraphNodeAnomalyDectionDataset('Cora', p = 15, k = 50)
>>> print(dataset[0])

property anomalies_idx

Functions that return the index of anomaly node

Returns: out – index of anomaly node
Return type: torch.Tensor

property anomaly_label

Functions that return the anomaly label of node: 0: normal node 1: structural anomaly 2: contextual anomaly

Returns: out – anomaly label of node
Return type: torch.Tensor

property contextual_anomalies_idx

Functions that return the index of contextual anomaly node

Returns: out – index of contextual anomaly node
Return type: torch.Tensor

evaluation_multiround(predict_score_arr)[source]

Functions that evaluates mulit-round result(as CoLA)

Parameters

predict_score_arr (torch.Tensor) – prediction to be evaluated

Returns

final_score (numpy.float)
a_score (numpy.float)
s_score (numpy.float)

evalution(prediction)[source]

Functions that evaluate prediction for separate anomaly and global, auc as metrics

Parameters

prediction (torch.Tensor) – prediction to be evaluated

Returns

final_score (numpy.float)
a_score (numpy.float)
s_score (numpy.float)

init_anomaly_label(label=None)[source]

Functions that initialize the anomaly label of node

Parameters: label (torch.Tensor) – initial anomaly label of node in ‘anomaly_label’ (default None)
Return type: None

inject_contextual_anomalies()[source]: Functions that inject contextual anomaly

inject_structural_anomalies()[source]: Functions that inject structural anomaly

property node_attr

Functions that return the attribute of node

Returns: out – attribute of node
Return type: torch.Tensor

property normal_idx

Functions that return the index of normal node

Returns: out – index of normal node
Return type: torch.Tensor

property num_anomaly

Functions that return the number of anomaly node

Returns: out – number of anomaly node
Return type: int

property num_nodes

Functions that return the number of node

Returns: out – number of node
Return type: int

preprocess_features(features)[source]

Functions that process features, here norm in row

Parameters: features (torch.Tensor) – features to be processed
Return type: None

process()[source]: Undone, Useless

reset_anomaly_label()[source]: Functions that reset the anomaly label of node

set_anomaly_label(label)[source]

Functions that set the anomaly label of node

Parameters: attr (torch.Tensor) – anomaly label assigned to node in ‘anomaly_label’
Return type: None

set_node_attr(attr)[source]

Functions that set the attribute of node

Parameters: attr (torch.Tensor) – attribute assigned to node in ‘feat’
Return type: None

property structural_anomalies_idx

Functions that return the index of structural anomaly node

Returns: out – index of structural anomaly node
Return type: torch.Tensor

dgld.utils.dataset.test_custom_dataset()[source]: Functions that test custom dataset loading well