src.fairreckitlib.experiment.experiment_config

  1"""This module contains the experiment configurations.
  2
  3Classes:
  4
  5    ExperimentConfig: base experiment configuration.
  6    PredictorExperimentConfig: a prediction experiment configuration.
  7    RecommenderExperimentConfig: a recommender experiment configuration.
  8
  9This program has been developed by students from the bachelor Computer Science at
 10Utrecht University within the Software Project course.
 11© Copyright Utrecht University (Department of Information and Computing Sciences)
 12"""
 13
 14from abc import ABCMeta, abstractmethod
 15from dataclasses import dataclass
 16from typing import Any, Dict, List
 17
 18from ..core.config.config_yml import format_yml_config_dict_list, format_yml_config_list
 19from ..core.core_constants import KEY_NAME, KEY_TYPE, KEY_TOP_K, KEY_RATED_ITEMS_FILTER, \
 20    TYPE_PREDICTION, TYPE_RECOMMENDATION
 21from ..data.data_factory import KEY_DATA
 22from ..data.pipeline.data_config import DataMatrixConfig
 23from ..evaluation.evaluation_factory import KEY_EVALUATION
 24from ..evaluation.pipeline.evaluation_config import MetricConfig
 25from ..model.model_factory import KEY_MODELS
 26from ..model.pipeline.model_config import ModelConfig
 27
 28
 29@dataclass
 30class ExperimentConfig(metaclass=ABCMeta):
 31    """Base Experiment Configuration."""
 32
 33    datasets: List[DataMatrixConfig]
 34    models: Dict[str, List[ModelConfig]]
 35    evaluation: List[MetricConfig]
 36    name: str
 37
 38    @abstractmethod
 39    def get_type(self) -> str:
 40        """Get the type of the experiment configuration.
 41
 42        Returns:
 43            the experiment type.
 44        """
 45        raise NotImplementedError()
 46
 47    def to_yml_format(self) -> Dict[str, Any]:
 48        """Format experiment configuration to a yml compatible dictionary.
 49
 50        Returns:
 51            a dictionary containing the experiment configuration.
 52        """
 53        yml_format = {
 54            KEY_NAME: self.name,
 55            KEY_TYPE: self.get_type(),
 56            KEY_DATA: format_yml_config_list(self.datasets),
 57            KEY_MODELS: format_yml_config_dict_list(self.models)
 58        }
 59
 60        # only include evaluation if it is present
 61        if len(self.evaluation) > 0:
 62            yml_format[KEY_EVALUATION] = format_yml_config_list(self.evaluation)
 63
 64        return yml_format
 65
 66
 67@dataclass
 68class PredictorExperimentConfig(ExperimentConfig):
 69    """Prediction Experiment Configuration."""
 70
 71    def get_type(self) -> str:
 72        """Get the predictor experiment type.
 73
 74        Returns:
 75            TYPE_PREDICTION.
 76        """
 77        return TYPE_PREDICTION
 78
 79
 80@dataclass
 81class RecommenderExperimentConfig(ExperimentConfig):
 82    """Recommender Experiment Configuration."""
 83
 84    top_k: int
 85    rated_items_filter: bool
 86
 87    def get_type(self) -> str:
 88        """Get the recommender experiment type.
 89
 90        Returns:
 91            TYPE_RECOMMENDATION.
 92        """
 93        return TYPE_RECOMMENDATION
 94
 95    def to_yml_format(self) -> Dict[str, Any]:
 96        """Format recommender experiment configuration to a yml compatible dictionary.
 97
 98        Returns:
 99            a dictionary containing the experiment configuration.
100        """
101        yml_format = ExperimentConfig.to_yml_format(self)
102        yml_format[KEY_TOP_K] = self.top_k
103        yml_format[KEY_RATED_ITEMS_FILTER] = self.rated_items_filter
104        return yml_format