pygip.utils.metrics

Classes

AttackMetric()
ComputationMetric()
DefenseMetric()
GraphNeuralNetworkMetric([fidelity, ...])	Graph Neural Network Metric Class.
MetricBase()

class pygip.utils.metrics.AttackMetric

Bases: MetricBase

_abc_impl = <_abc_data object>

compute()

Compute and return all metric results.

update(preds, labels, target_outputs=None, surrogate_outputs=None)

Update internal metric state.

class pygip.utils.metrics.ComputationMetric

Bases: object

update()

class pygip.utils.metrics.DefenseMetric

Bases: MetricBase

_abc_impl = <_abc_data object>

compute()

Compute and return all metric results.

update(preds, labels, target_outputs=None, surrogate_outputs=None)

Update internal metric state.

update_defense_task(defense_preds, defense_labels)

update_defense_wm(wm_preds, wm_labels)

update_surrogate_task(surrogate_preds, surrogate_labels)

update_surrogate_wm(wm_preds, wm_labels)

update_target(target_preds, target_labels)

class pygip.utils.metrics.GraphNeuralNetworkMetric(fidelity=0, accuracy=0, model=None, graph=None, features=None, mask=None, labels=None, query_labels=None)

Bases: object

Graph Neural Network Metric Class.

This class evaluates two metrics, fidelity and accuracy, for a given GNN model on a specified graph and features.

static calculate_surrogate_fidelity(target_model, surrogate_model, data, mask=None)

Calculate fidelity between target and surrogate model predictions.

Parameters:

• target_model – Original model

• surrogate_model – Extracted surrogate model

• data – Input graph data

• mask – Optional mask for evaluation on specific nodes

Returns:

Fidelity score (percentage of matching predictions)

Return type:

float

evaluate()

Main function to update fidelity and accuracy scores.

evaluate_helper(model, graph, features, labels, mask)

Helper function to evaluate the model’s performance.

static evaluate_surrogate_extraction(target_model, surrogate_model, data, train_mask=None, val_mask=None, test_mask=None)

Comprehensive evaluation of surrogate extraction attack.

Parameters:

• target_model – Original model

• surrogate_model – Extracted surrogate model

• data – Input graph data

• train_mask – Mask for training nodes

• val_mask – Mask for validation nodes

• test_mask – Mask for test nodes

Returns:

Dictionary containing fidelity scores for different data splits

Return type:

dict

class pygip.utils.metrics.MetricBase

Bases: ABC

_abc_impl = <_abc_data object>

abstract compute()

Compute and return all metric results.

static compute_auc(preds, labels)

static compute_default_metrics(preds, labels)

static compute_fidelity(output1, output2)

print()

abstract reset()

Reset internal state.

abstract update(preds, labels, **kwargs)

Update internal metric state.