Python SVDNeighbourhood.load_data Examples

Example #1

File: algorithms_old.py Project: andrewponomarev/PycharmProjects

class KNNPlusSVD_lib:
    def __init__(self, filename, K):
        self.svd = SVDNeighbourhood()
        self.K = K
        self.svd.load_data(filename ,  sep='	', format={'col':0, 'row':1, 'value':2, 'ids': int})

    def predict(self, userId, itemId):
        self.svd.compute(self.K, min_values=5, pre_normalize='all' , mean_center=True, post_normalize=None)
        r = self.svd.predict(11, 33, weighted=True, MIN_VALUE=1.0, MAX_VALUE=5.0)
        return r

Example #2

File: rec_sys.py Project: mmetelytsia/movie_recommender_system

    # recommender = Recommender()
    # recommender.load_web_data('dataset',
    #                           [{'Запах женщины': 9, 'The Usual Suspects': 8, 'The Departed': 8,
    #                             'Тутси': 7, 'Выпускник': 10, 'Залечь на дно в Брюгге': 4, 'Евротур': 7,
    #                             'Goodfellas': 6, 'Донни Браско': 8, 'Амели': 3, 'Идиократия': 7}],
    #                           100, 0, 10, 10)

    # recommender.load_local_data('dataset', K=100, min_values=0)
    # m = recommender.matrix.get_rating_matrix()
    #
    # m1 = recommender.get_predictions_for_all_users()

    from recsys.algorithm.factorize import SVDNeighbourhood

    svd = SVDNeighbourhood()
    svd.load_data('test_dataset',
                  sep=' ',
                  format={
                      'col': 1,
                      'row': 0,
                      'value': 2,
                      'ids': int
                  })
    svd.compute(100, 0)
    print svd.predict(108, 698)

    # svd.load_data(filename=sys.argv[1], sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
    # K=100
    # svd.compute(k=K, min_values=5, pre_normalize=None, mean_center=True, post_normalize=True)

Example #3

File: rec_sys.py Project: Iodina/movie_recommender_system

if __name__ == "__main__":

    # recommender = Recommender()
    # recommender.load_web_data('dataset',
    #                           [{'Запах женщины': 9, 'The Usual Suspects': 8, 'The Departed': 8,
    #                             'Тутси': 7, 'Выпускник': 10, 'Залечь на дно в Брюгге': 4, 'Евротур': 7,
    #                             'Goodfellas': 6, 'Донни Браско': 8, 'Амели': 3, 'Идиократия': 7}],
    #                           100, 0, 10, 10)

    # recommender.load_local_data('dataset', K=100, min_values=0)
    # m = recommender.matrix.get_rating_matrix()
    #
    # m1 = recommender.get_predictions_for_all_users()


    from recsys.algorithm.factorize import SVDNeighbourhood

    svd = SVDNeighbourhood()
    svd.load_data('test_dataset', sep=' ', format={'col': 1, 'row': 0, 'value': 2, 'ids': int})
    svd.compute(100, 0)
    print svd.predict(108, 698)

    # svd.load_data(filename=sys.argv[1], sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
    # K=100
    # svd.compute(k=K, min_values=5, pre_normalize=None, mean_center=True, post_normalize=True)

Example #4

File: kNN+SVD_old.py Project: andrewponomarev/PycharmProjects

__author__ = 'ponomarevandrew'


from recsys.algorithm.factorize import SVDNeighbourhood

svd = SVDNeighbourhood()
svd.load_data(filename='ml-100k/u1.base',  sep='	', format={'col':0, 'row':1, 'value':2, 'ids': int})
K=30

svd.compute(k=K, min_values=5, pre_normalize='all' , mean_center=True, post_normalize=None)

print(svd.predict(11, 33, weighted=True, MIN_VALUE=1.0, MAX_VALUE=5.0))