src.fairreckitlib.model.algorithms.elliot.elliot_params

  1"""This module contains the parameter creation functions for elliot recommenders.
  2
  3Functions:
  4
  5    create_params_funk_svd: create FunkSVD config parameters.
  6    create_params_knn: create ItemKNN/UserKNN config parameters.
  7    create_params_multi_vae: create MultiVAE config parameters.
  8    create_params_pure_svd: create PureSVD config parameters.
  9    create_params_svd_pp: create SVDpp config parameters.
 10
 11This program has been developed by students from the bachelor Computer Science at
 12Utrecht University within the Software Project course.
 13© Copyright Utrecht University (Department of Information and Computing Sciences)
 14"""
 15
 16from ....core.config.config_parameters import ConfigParameters
 17
 18
 19def create_params_funk_svd() -> ConfigParameters:
 20    """Create the parameters of the FunkSVD algorithm.
 21
 22    Returns:
 23        the configuration parameters of the algorithm.
 24    """
 25    params = ConfigParameters()
 26    params.add_number('iterations', int, 10, (1, 50))
 27    params.add_number('factors', int, 10, (1, 100))
 28    params.add_number('learning_rate', float, 0.001, (0.0001, 1.0))
 29    params.add_number('regularization_factors', float, 0.1, (0.0001, 1.0))
 30    params.add_number('regularization_bias', float, 0.001, (0.0001, 1.0))
 31    params.add_random_seed('seed')
 32    return params
 33
 34
 35def create_params_knn() -> ConfigParameters:
 36    """Create the parameters of the ItemKNN/UserKNN algorithm.
 37
 38    Returns:
 39        the configuration parameters of the algorithm.
 40    """
 41    implementations = ['aiolli', 'classical']
 42    similarities = [
 43        'cosine',
 44        'adjusted',
 45        'asymmetric',
 46        'pearson',
 47        'jaccard',
 48        'dice',
 49        'tversky',
 50        'tanimoto'
 51    ]
 52
 53    params = ConfigParameters()
 54    params.add_number('neighbors', int, 40, (1, 50))
 55    params.add_single_option('similarity', str, similarities[0], similarities)
 56    params.add_single_option('implementation', str, implementations[0], implementations)
 57    return params
 58
 59
 60def create_params_multi_vae() -> ConfigParameters:
 61    """Create the parameters of the MultiVAE algorithm.
 62
 63    Returns:
 64        the configuration parameters of the algorithm.
 65    """
 66    params = ConfigParameters()
 67    params.add_number('iterations', int, 10, (1, 50))
 68    params.add_number('factors', int, 200, (100, 500))
 69    params.add_number('learning_rate', float, 0.001, (0.0001, 1.0))
 70    params.add_number('regularization_factors', float, 0.01, (0.0001, 1.0))
 71    params.add_number('intermediate_dimensions', int, 100, (1000, 600))
 72    params.add_number('dropout_probability', float, 1.0, (0.0, 1.0))
 73    params.add_random_seed('seed')
 74    return params
 75
 76
 77def create_params_pure_svd() -> ConfigParameters:
 78    """Create the parameters of the PureSVD algorithm.
 79
 80    Returns:
 81        the configuration parameters of the algorithm.
 82    """
 83    params = ConfigParameters()
 84    params.add_number('factors', int, 10, (1, 100))
 85    params.add_random_seed('seed')
 86    return params
 87
 88
 89def create_params_svd_pp() -> ConfigParameters:
 90    """Create the parameters of the SVDpp algorithm.
 91
 92    Returns:
 93        the configuration parameters of the algorithm.
 94    """
 95    params = ConfigParameters()
 96    params.add_number('iterations', int, 10, (1, 50))
 97    params.add_number('factors', int, 50, (1, 100))
 98    params.add_number('learning_rate', float, 0.001, (0.0001, 1.0))
 99    params.add_number('regularization_factors', float, 0.1, (0.0001, 1.0))
100    params.add_number('regularization_bias', float, 0.001, (0.0001, 1.0))
101    params.add_random_seed('seed')
102    return params