cf1f601ab89d89ec72ba009140470d4689553db1,caserec/recommenders/rating_prediction/user_attribute_knn.py,UserAttributeKNN,__init__,#UserAttributeKNN#Any#Any#Any#Any#Any#Any#Any#Any#,46

Before Change

class UserAttributeKNN(UserKNN):
    def __init__(self, train_file, test_file, metadata_file=None, similarity_matrix_file=None, prediction_file=None,
                 neighbors=30, similarity_metric="correlation", space_type="\t"):
        UserKNN.__init__(self, train_file, test_file, prediction_file=prediction_file, neighbors=neighbors,
                         similarity_metric=similarity_metric, space_type=space_type)

        if metadata_file is None and similarity_matrix_file is None:
            print("This algorithm needs a similarity matrix or a metadata file!")
            sys.exit(0)

        if metadata_file is not None:
            self.metadata = ReadFile(metadata_file, space_type).read_metadata(self.users)
            self.matrix = self.metadata["matrix"]
        self.similarity_matrix_file = similarity_matrix_file

    def read_matrix(self):

After Change

    User-Attribute-kNN predicts a user’s rating according to how similar users rated the same item. The algorithm
    matches similar users based on the similarity of their attributes scores. However, instead of traditional UserKNN,
    this approach uses a pre-computed similarity matrix based on metadata.




// © 2018. Case Recommender (MIT License)

import numpy as np

from caserec.recommenders.rating_prediction.userknn import UserKNN
from caserec.utils.process_data import ReadFile

__author__ = "Arthur Fortes <fortes.arthur@gmail.com>"


class UserAttributeKNN(UserKNN):
    def __init__(self, train_file=None, test_file=None, output_file=None, metadata_file=None, similarity_file=None,
                 k_neighbors=30, as_similar_first=True, metadata_as_binary=False,
                 metadata_similarity_sep="\t", similarity_metric="cosine", sep="\t", output_sep="\t"):
        
        User Attribute KNN for Rating Prediction

        This algorithm predicts a rating for each pair (user, item) based on the similar items that his neighbors
        (similar users) consumed, using a metadata or similarity pre-computed file

        Usage::

            >> UserAttributeKNN(train, test, similarity_file=sim_matrix, as_similar_first=True).compute()
            >> UserAttributeKNN(train, test, metadata_file=metadata, as_similar_first=True).compute()

        :param train_file: File which contains the train set. This file needs to have at least 3 columns
        (user item feedback_value).
        :type train_file: str

        :param test_file: File which contains the test set. This file needs to have at least 3 columns
        (user item feedback_value).
        :type test_file: str, default None

        :param output_file: File with dir to write the final predictions
        :type output_file: str, default None

        :param metadata_file: File which contains the metadata set. This file needs to have at least 2 columns
        (user metadata).
        :type metadata_file: str, default None

Instances