src.fairreckitlib.model.algorithms.base_algorithm

  1"""This module contains the base class for all algorithms.
  2
  3Classes:
  4
  5    BaseAlgorithm: the base class for algorithms.
  6
  7This program has been developed by students from the bachelor Computer Science at
  8Utrecht University within the Software Project course.
  9© Copyright Utrecht University (Department of Information and Computing Sciences)
 10"""
 11
 12from abc import ABCMeta, abstractmethod
 13from typing import Any, Dict, Optional
 14
 15from .matrix import Matrix
 16
 17
 18class BaseAlgorithm(metaclass=ABCMeta):
 19    """Base class for FairRecKit algorithms.
 20
 21    An algorithm is used for carrying out recommender system experiments.
 22    Derived algorithms are expected to implement the abstract interface.
 23
 24    Abstract methods:
 25
 26    on_train
 27
 28    Public methods:
 29
 30    get_name
 31    get_num_threads
 32    get_params
 33    get_train_set
 34    train
 35    """
 36
 37    def __init__(self):
 38        """Construct the algorithm."""
 39        self.train_set = None
 40
 41    @abstractmethod
 42    def get_name(self) -> str:
 43        """Get the name of the algorithm.
 44
 45        Returns:
 46            the algorithm name.
 47        """
 48        raise NotImplementedError()
 49
 50    @abstractmethod
 51    def get_num_threads(self) -> int:
 52        """Get the max number of threads the algorithm can use.
 53
 54        Returns:
 55            the number of threads.
 56        """
 57        raise NotImplementedError()
 58
 59    @abstractmethod
 60    def get_params(self) -> Dict[str, Any]:
 61        """Get the parameters of the algorithm.
 62
 63        Returns:
 64            the algorithm parameters.
 65        """
 66        raise NotImplementedError()
 67
 68    def get_train_set(self) -> Optional[Matrix]:
 69        """Get the train set that the algorithm was trained on.
 70
 71        Returns:
 72            the train set matrix or None if the algorithm is not trained yet.
 73        """
 74        return self.train_set
 75
 76    def train(self, train_set: Matrix) -> None:
 77        """Train the algorithm on the specified train set.
 78
 79        Args:
 80            train_set: the matrix train set.
 81
 82        Raises:
 83            ArithmeticError: possibly raised by an algorithm on training.
 84            MemoryError: possibly raised by an algorithm on training.
 85            RuntimeError: possibly raised by an algorithm on training.
 86            TypeError: when the specified train set does not have the correct matrix format.
 87        """
 88        self.train_set = train_set
 89        self.on_train(self.train_set.get_matrix())
 90
 91    @abstractmethod
 92    def on_train(self, train_set: Any) -> None:
 93        """Train the algorithm on the train set.
 94
 95        Derived classes should implement the training logic
 96        of the algorithm. The train set can be anything depending
 97        on the matrix that is used.
 98
 99        Args:
100            train_set: the set to train the algorithm with.
101        """
102        raise NotImplementedError()