hopwise.evaluator.register¶

Attributes¶

metric_module_name

Classes¶

`Register`	Register module load the registry according to the metrics in config.
`Register_KG`	Register module load the registry according to the metrics in config.

Functions¶

cluster_info(module_name)

Collect information of all metrics, including:

Module Contents¶

hopwise.evaluator.register.cluster_info(module_name)¶

Collect information of all metrics, including:

metric_need: Information needed to calculate this metric, the combination of rec.items, rec.topk, rec.meanrank, rec.score, data.num_items, data.num_users, data.count_items, data.count_users, data.label.

metric_type: Whether the scores required by metric are grouped by user, range in EvaluatorType.RANKING and EvaluatorType.VALUE.

smaller: Whether the smaller metric value represents better performance, range in True and False, default to False.

Note

For metric_type: in current hopwise, all the “grouped-score” metrics are ranking-based and all the “non-grouped-score” metrics are value-based. To keep with our paper, we adopted the more formal terms: RANKING and VALUE.

Parameters:: module_name (str) – the name of module hopwise.evaluator.metrics.
Returns:: Three dictionaries containing the above information and a dictionary matching metric names to metric classes.
Return type:: dict

hopwise.evaluator.register.metric_module_name = 'hopwise.evaluator.metrics'¶

class hopwise.evaluator.register.Register(config)¶

Register module load the registry according to the metrics in config. It is a member of DataCollector. The DataCollector collect the resource that need for Evaluator under the guidance of Register

config¶

metrics¶

_build_register()¶

has_metric(metric: str)¶

need(key: str)¶

class hopwise.evaluator.register.Register_KG(config)¶

Bases: Register

Register module load the registry according to the metrics in config. It is a member of DataCollector. The DataCollector collect the resource that need for Evaluator under the guidance of Register

metrics¶