def fit(self,
topK=50,
shrink=100,
similarity='cosine',
normalize=True,
feature_weighting="none",
**similarity_args):
self.topK = topK
self.shrink = shrink
if feature_weighting not in self.FEATURE_WEIGHTING_VALUES:
raise ValueError(
"Value for 'feature_weighting' not recognized. Acceptable values are {}, provided was '{}'"
.format(self.FEATURE_WEIGHTING_VALUES, feature_weighting))
if feature_weighting == "BM25":
self.URM_train = self.URM_train.astype(np.float32)
self.URM_train = okapi_BM_25(self.URM_train.T).T
self.URM_train = check_matrix(self.URM_train, 'csr')
elif feature_weighting == "TF-IDF":
self.URM_train = self.URM_train.astype(np.float32)
self.URM_train = TF_IDF(self.URM_train.T).T
self.URM_train = check_matrix(self.URM_train, 'csr')
similarity = Compute_Similarity(self.URM_train.T,
shrink=shrink,
topK=topK,
normalize=normalize,
similarity=similarity,
**similarity_args)
self.W_sparse = similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def __init__(self,
urm_train,
icm,
urm_per_region_list,
urm_per_age_list,
weights,
add_pure_svd=False,
add_slim_bpr=False):
self.urm_train = urm_train
self.urm_per_region_list = urm_per_region_list
self.urm_per_age_list = urm_per_age_list
self.icm = icm
self.add_pure_svd = add_pure_svd
self.add_slim_bpr = add_slim_bpr
self.weights = weights
self.icfknn_list = []
self.ucfknn_list = []
self.icbfknn_list = []
self.icm_bm25 = self.icm.copy().astype(np.float32)
self.icm_bm25 = okapi_BM_25(self.icm_bm25)
self.icm_bm25 = self.icm_bm25.tocsr()
self.ratings = None
self.extractor = Extractor()
# Creation of the list of algortms that have to be used
if self.urm_per_region_list is not None:
for urm in self.urm_per_region_list:
sps.csr_matrix(urm)
self.icfknn_list.append(ItemCFKNNRecommender(urm.copy()))
self.ucfknn_list.append(UserCFKNNRecommender(urm.copy()))
self.icbfknn_list.append(
ItemCBFKNNRecommender(urm.copy(), self.icm_bm25.copy()))
if self.urm_per_age_list is not None:
for urm in self.urm_per_age_list:
sps.csr_matrix(urm)
self.icfknn_list.append(ItemCFKNNRecommender(urm.copy()))
self.ucfknn_list.append(UserCFKNNRecommender(urm.copy()))
self.icbfknn_list.append(
ItemCBFKNNRecommender(urm.copy(), self.icm_bm25.copy()))
# self.icfknn_list.append(ItemCFKNNRecommender(self.urm_train.copy()))
# self.ucfknn_list.append(UserCFKNNRecommender(self.urm_train.copy()))
# self.icbfknn_list.append(ItemCBFKNNRecommender(self.urm_train.copy(), self.icm_bm25.copy()))
if self.add_pure_svd:
self.pure_SVD = PureSVDRecommender(self.urm_train.copy())
if self.add_slim_bpr:
self.slim_bpr = SLIM_BPR_Cython(self.urm_train.copy())
def __init__(self, urm, icm, p_icfknn, p_ucfknn, p_cbfknn, p_slimbpr, p_puresvd, p_als, p_cfw, weights):
# Parameter saving
self.p_icfknn = p_icfknn
self.p_ucfknn = p_ucfknn
self.p_cbfknn = p_cbfknn
self.p_slimbpr = p_slimbpr
self.p_puresvd = p_puresvd
self.p_als = p_als
self.p_cfw = p_cfw
self.weights = weights
# Getting matrices
self.urm = urm
self.icm = icm
self.icm_bm25 = self.icm.copy().astype(np.float32)
self.icm_bm25 = okapi_BM_25(self.icm_bm25)
self.icm_bm25 = self.icm_bm25.tocsr()
# Creating recommenders
if self.p_icfknn is not None:
self.recommender_itemCFKNN = ItemCFKNNRecommender(self.urm.copy())
if self.p_ucfknn is not None:
self.recommender_userCFKNN = UserCFKNNRecommender(self.urm.copy())
if self.p_cbfknn is not None:
self.recommender_itemCBFKNN = ItemCBFKNNRecommender(self.urm.copy(), self.icm_bm25)
if self.p_slimbpr is not None:
self.recommender_slim_bpr = SLIM_BPR_Cython(self.urm.copy())
if self.p_puresvd is not None:
self.recommender_puresvd = PureSVDRecommender(self.urm.copy())
if self.p_als is not None:
self.recommender_als = AlternatingLeastSquare(self.urm.copy())
_, n_items = urm.shape
self.hybrid_ratings = np.zeros(n_items)