gigl.experimental.knowledge_graph_embedding.lib.config.evaluation

Classes

EvaluationPhaseConfig

Configuration for evaluation phases (validation/testing) during knowledge graph embedding training.

Module Contents

class gigl.experimental.knowledge_graph_embedding.lib.config.evaluation.EvaluationPhaseConfig

Configuration for evaluation phases (validation/testing) during knowledge graph embedding training.

Controls how model performance is measured during training (validation phase) and after training completion (testing phase). Uses ranking-based metrics to assess link prediction quality.

dataloader

Configuration for data loading during evaluation (workers, memory pinning). Defaults to DataloaderConfig() with standard settings.

Type:

DataloaderConfig

step_frequency

How often to run evaluation during training (every N steps). If None, evaluation runs only at the end of training. Defaults to None.

Type:

Optional[int]

num_batches

Maximum number of batches to evaluate. Useful for faster evaluation on large datasets by sampling a subset. If None, evaluates all data. Defaults to None.

Type:

Optional[int]

hit_rates_at_k

List of k values for computing Hit@k (Hits at k) metrics. Hit@k measures if the correct answer appears in the top k predictions. Common values are [1, 10, 100]. Defaults to [1, 10, 100].

Type:

List[int]

sampling

Negative sampling configuration for evaluation. Should match or be compatible with training sampling to ensure fair comparison. Defaults to SamplingConfig() with standard settings.

Type:

SamplingConfig

dataloader: gigl.experimental.knowledge_graph_embedding.lib.config.dataloader.DataloaderConfig

hit_rates_at_k: List[int] = [1, 10, 100]

num_batches: int | None = None

sampling: gigl.experimental.knowledge_graph_embedding.lib.config.sampling.SamplingConfig

step_frequency: int | None = None