def __init__(self, urm_train, eurm=False):
super(HybridGenRecommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
data = DataManager()
urm_train = check_matrix(urm_train.copy(), 'csr')
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(shrink=40, topK=20, feature_weighting='BM25')
# recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
# recommender_2.fit(shrink=500, topK=1600, normalize=True)
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
def __init__(self, urm_train, eurm=False):
super(HybridNormOrigRecommender, self).__init__(urm_train)
self.data_folder = Path(__file__).parent.parent.absolute()
self.eurm = eurm
self.num_users = urm_train.shape[0]
data = DataManager()
urm_train = check_matrix(urm_train.copy(), 'csr')
icm_price, icm_asset, icm_sub, icm_all = data.get_icm()
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(shrink=40, topK=20, feature_weighting='BM25')
recommender_7 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_7.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
# recommender_1 = HybridGenRecommender(urm_train, eurm=self.eurm)
# recommender_1.fit()
# recommender_2 = ItemKNNCFRecommender(urm_train)
# recommender_2.fit(shrink=30, topK=20)
recommender_2 = ItemKNNCFRecommender(urm_train)
recommender_2.fit(topK=5,
shrink=500,
feature_weighting='BM25',
similarity='tversky',
normalize=False,
tversky_alpha=0.0,
tversky_beta=1.0)
recommender_3 = UserKNNCFRecommender(urm_train)
recommender_3.fit(shrink=2, topK=600, normalize=True)
# recommender_3 = UserKNNCFRecommender(urm_train)
# recommender_3.fit(topK=697, shrink=1000, feature_weighting='TF-IDF', similarity='tversky', normalize=False,
# tversky_alpha=1.0, tversky_beta=1.0)
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16,
alpha=0.03374950051351756,
beta=0.24087176329409027,
normalize_similarity=False)
recommender_5 = SLIM_BPR_Cython(urm_train)
recommender_5.fit(lambda_i=0.0926694015,
lambda_j=0.001697250,
learning_rate=0.002391,
epochs=65,
topK=200)
recommender_6 = ALSRecommender(urm_train)
recommender_6.fit(alpha=5, iterations=40, reg=0.3)
self.recommender_1 = recommender_1
self.recommender_2 = recommender_2
self.recommender_3 = recommender_3
self.recommender_4 = recommender_4
self.recommender_5 = recommender_5
self.recommender_6 = recommender_6
self.recommender_7 = recommender_7
if self.eurm:
if Path(self.data_folder / 'Data/uicm_orig_sparse.npz').is_file():
print("Previous uicm_sparse found")
self.score_matrix_1 = sps.load_npz(self.data_folder /
'Data/uicm_sparse.npz')
else:
print("uicm_sparse not found, create new one...")
self.score_matrix_1 = self.recommender_1._compute_item_matrix_score(
np.arange(self.num_users))
sps.save_npz(self.data_folder / 'Data/uicm_orig_sparse.npz',
self.score_matrix_1)
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_3 = self.recommender_3._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_4 = self.recommender_4._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_5 = self.recommender_5._compute_item_matrix_score(
np.arange(self.num_users))
self.score_matrix_6 = self.recommender_6._compute_item_score(
np.arange(self.num_users))
self.score_matrix_1 = normalize(self.score_matrix_2,
norm='max',
axis=1)
self.score_matrix_2 = normalize(self.score_matrix_2,
norm='max',
axis=1)
self.score_matrix_3 = normalize(self.score_matrix_3,
norm='max',
axis=1)
self.score_matrix_4 = normalize(self.score_matrix_4,
norm='max',
axis=1)
self.score_matrix_5 = normalize(self.score_matrix_5,
norm='max',
axis=1)
self.score_matrix_6 = normalize(self.score_matrix_6,
norm='max',
axis=1)
from DataManager.split_train_validation_leave_k_out import split_train_leave_k_out_user_wise
from FeatureWeighting.CFW_D_Similarity_Linalg import CFW_D_Similarity_Linalg
from FeatureWeighting.User_CFW_D_Similarity_Linalg import User_CFW_D_Similarity_Linalg
from Utils.s_plus import dot_product
import scipy.sparse as sps
from Base.Evaluation.Evaluator import EvaluatorHoldout
from GraphBased.RP3betaRecommender import RP3betaRecommender
from ParameterTuning.SearchBayesianSkopt import SearchBayesianSkopt
from skopt.space import Real, Integer, Categorical
from ParameterTuning.SearchAbstractClass import SearchInputRecommenderArgs
Data = DataManager()
ucm_age, ucm_region, ucm_all = Data.get_ucm()
icm_price, icm_asset, icm_sub, icm_all = Data.get_icm()
urm_train, urm_test = split_train_leave_k_out_user_wise(Data.get_urm(),
temperature='normal')
urm_train, urm_valid = split_train_leave_k_out_user_wise(urm_train,
temperature='valid')
evaluator_test = EvaluatorHoldout(urm_test, cutoff_list=[10])
evaluator_valid = EvaluatorHoldout(urm_valid, cutoff_list=[10])
recommender_4 = RP3betaRecommender(urm_train)
recommender_4.fit(topK=16,
alpha=0.03374950051351756,
beta=0.24087176329409027,
normalize_similarity=True)
# recommender_3 = UserKNNCFRecommender(urm_train)
def __init__(self, urm_train, eurm=False):
super(HybridGen2Recommender, self).__init__(urm_train)
self.eurm = eurm
self.data_folder = Path(__file__).parent.parent.absolute()
self.num_users = urm_train.shape[0]
urm_train = check_matrix(urm_train.copy(), 'csr')
data = DataManager()
if Path(self.data_folder /
'Data/icm_sparse.npz').is_file() and self.eurm:
print("Previous icm_sparse found")
else:
_, _, _, icm_all = data.get_icm()
args = {
'topK': 6,
'shrink': 5,
'feature_weighting': 'TF-IDF',
'similarity': 'cosine',
'normalize': False
}
recommender_1 = ItemKNNCBFRecommender(urm_train, icm_all)
recommender_1.fit(**args)
self.recommender_1 = recommender_1
if Path(self.data_folder /
'Data/ucm_sparse.npz').is_file() and self.eurm:
print("Previous ucm_sparse found")
else:
ucm_age, ucm_region, ucm_all = data.get_ucm()
recommender_2 = UserKNNCBFRecommender(urm_train, ucm_all)
recommender_2.fit(shrink=1777,
topK=1998,
similarity='tversky',
feature_weighting='BM25',
tversky_alpha=0.1604953616,
tversky_beta=0.9862348646)
self.recommender_2 = recommender_2
if self.eurm:
beta = 0.6
if Path(self.data_folder / 'Data/icm_sparse.npz').is_file():
self.score_matrix_1 = sps.load_npz(self.data_folder /
'Data/icm_sparse.npz')
else:
print("icm_sparse not found, create new one...")
self.score_matrix_1 = self.recommender_1._compute_item_matrix_score(
np.arange(self.num_users))
user_score_matrix_1 = normalize(self.score_matrix_1,
norm='max',
axis=1)
item_score_matrix_1 = normalize(self.score_matrix_1.tocsc(),
norm='max',
axis=0)
self.score_matrix_1 = item_score_matrix_1 * beta + user_score_matrix_1.tocsr(
) * (1 - beta)
sps.save_npz(self.data_folder / 'Data/icm_sparse.npz',
self.score_matrix_1)
if Path(self.data_folder / 'Data/ucm_sparse.npz').is_file():
self.score_matrix_2 = sps.load_npz(self.data_folder /
'Data/ucm_sparse.npz')
else:
print("ucm_sparse not found, create new one...")
self.score_matrix_2 = self.recommender_2._compute_item_matrix_score(
np.arange(self.num_users))
user_score_matrix_2 = normalize(self.score_matrix_2,
norm='max',
axis=1)
item_score_matrix_2 = normalize(self.score_matrix_2.tocsc(),
norm='max',
axis=0)
self.score_matrix_2 = item_score_matrix_2 * beta + user_score_matrix_2.tocsr(
) * (1 - beta)
sps.save_npz(self.data_folder / 'Data/ucm_sparse.npz',
self.score_matrix_2)
