Source code for recbole.evaluator.register

# @Time   : 2021/6/23
# @Author : Zihan Lin
# @Email  : zhlin@ruc.edu.cn

# UPDATE
# @Time   : 2021/8/29
# @Author : Zhichao Feng
# @email  : fzcbupt@gmail.com

"""
recbole.evaluator.register
################################################
"""
import inspect
import sys


[docs]def 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 RecBole, 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``. Args: module_name (str): the name of module ``recbole.evaluator.metrics``. Returns: dict: Three dictionaries containing the above information and a dictionary matching metric names to metric classes. """ smaller_m = [] m_dict, m_info, m_types = {}, {}, {} metric_class = inspect.getmembers( sys.modules[module_name], lambda x: inspect.isclass(x) and x.__module__ == module_name, ) for name, metric_cls in metric_class: name = name.lower() m_dict[name] = metric_cls if hasattr(metric_cls, "metric_need"): m_info[name] = metric_cls.metric_need else: raise AttributeError(f"Metric '{name}' has no attribute [metric_need].") if hasattr(metric_cls, "metric_type"): m_types[name] = metric_cls.metric_type else: raise AttributeError(f"Metric '{name}' has no attribute [metric_type].") if metric_cls.smaller is True: smaller_m.append(name) return smaller_m, m_info, m_types, m_dict
metric_module_name = "recbole.evaluator.metrics" smaller_metrics, metric_information, metric_types, metrics_dict = cluster_info( metric_module_name )
[docs]class Register(object): """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 """ def __init__(self, config): self.config = config self.metrics = [metric.lower() for metric in self.config["metrics"]] self._build_register() def _build_register(self): for metric in self.metrics: metric_needs = metric_information[metric] for info in metric_needs: setattr(self, info, True)
[docs] def has_metric(self, metric: str): if metric.lower() in self.metrics: return True else: return False
[docs] def need(self, key: str): if hasattr(self, key): return getattr(self, key) return False