def __init__(self, data: DataObject):
super(Hybrid100AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCBFRecommender(data.ucm_all, data.urm_train)
self.rec2 = MatrixFactorization_AsySVD_Cython(data.urm_train)
self.random_seed = data.random_seed
try:
self.rec1.load_model(
"stored_recommenders/user_cbf/",
f"{self.random_seed}_topK=5000_shrink=5_feature_weighting=TF-IDF_similarity=euclidean"
)
except:
self.rec1.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
self.rec1.save_model(
"stored_recommenders/user_cbf/",
f"{self.random_seed}_topK=5000_shrink=5_feature_weighting=TF-IDF_similarity=euclidean"
)
self.rec2.load_model("Hybrid", "AsySVD")
cold = data.ids_cold_user
train_cold = data.urm_train_users_by_type[0][1]
# if train_cold.shape[0] > 0:
# target_users = np.append(cold, train_cold)
# else:
# target_users = cold
target_users = data.ids_user
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2],
recommended_users=target_users,
max_cutoff=30)
def __init__(self,
data: DataObject,
k: int,
leave_k_out=0,
threshold=0,
probability=0.2):
super(Hybrid400AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.max_cutoff = 30
rec = ItemKNNCBFRecommender(data.urm_train, data.icm_all_augmented)
rec.fit(topK=10)
print(f"Not augmented {data.augmented_urm.nnz}")
data.augmented_urm = augment_with_item_similarity_best_scores(
data.augmented_urm,
rec.W_sparse,
500,
value=0.3,
remove_seen=False)
# print(f"After User CBF {data.augmented_urm.nnz}")
# data.augmented_urm = augment_with_best_recommended_items(data.augmented_urm, rec,
# data.urm_train_users_by_type[1][1], 1, value=0.2)
#
print(f"After Item CBF {data.augmented_urm.nnz}")
rec = Hybrid100AlphaRecommender(data)
rec.fit()
data.augmented_urm = augment_with_best_recommended_items(
data.augmented_urm,
rec,
data.urm_train_users_by_type[0][1],
1,
value=1)
# data.augmented_urm = augment_with_best_recommended_items(data.augmented_urm, rec,
# data.ids_warm_train_users, 2, value=0.1)
print(f"After User CBF {data.augmented_urm.nnz}")
# print(f"After User CBF {data.augmented_urm.nnz}")
rec = None
recs = Parallel(n_jobs=6)(delayed(
par(data,
leave_k_out=leave_k_out,
threshold=threshold,
probability=probability).split_and_fit)(i) for i in range(k))
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=recs,
recommended_users=data.ids_user,
max_cutoff=self.max_cutoff)
self.hybrid_rec.weights = np.array([
np.sqrt(np.array(fib(30)[::-1])).astype(np.int).tolist()
for _ in range(k)
])
def __init__(self, data: DataObject):
super(Hybrid109AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = RP3betaRecommender(data.urm_train)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec1.fit(topK=20, alpha=0.11, beta=0.18)
self.rec2.fit(topK=18, shrink=850, similarity='jaccard', feature_weighting='BM25')
cold = data.ids_cold_user
self.target_users = data.urm_train_users_by_type[10][1]
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2],
recommended_users=self.target_users, max_cutoff=30)
def __init__(self, data: DataObject):
super(Hybrid104AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = RP3betaRecommender(data.urm_train)
self.rec1.fit(topK=26, alpha=0.25, beta=0.21)
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec2.fit(topK=10, shrink=1000, similarity="tanimoto", feature_weighting="BM25")
target_users = data.urm_train_users_by_type[4][1]
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2],
recommended_users=target_users, max_cutoff=30)
def __init__(self, data: DataObject, k: int, leave_k_out=0, threshold=0, probability=0.2):
super(Hybrid500AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.max_cutoff = 30
recs = Parallel(n_jobs=16)(
delayed(par(data,
leave_k_out=leave_k_out,
threshold=threshold,
probability=probability).split_and_fit)
(i)
for i in range(k))
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=recs,
recommended_users=data.ids_user, max_cutoff=self.max_cutoff)
def __init__(self, data: DataObject):
super(Hybrid102AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCFRecommender(data.urm_train)
self.rec1.fit(topK=1000, shrink=4500, similarity="cosine", feature_weighting="TF-IDF")
self.rec2 = ItemKNNCFRecommender(data.urm_train)
self.rec2.fit(topK=2000, shrink=800, similarity="cosine", feature_weighting="TF-IDF")
self.rec3 = SLIM_BPR_Cython(data.urm_train)
self.rec3.fit(epochs=120, topK=800, lambda_i=0.1, lambda_j=0.1, learning_rate=0.0001)
self.rec4 = RP3betaRecommender(data.urm_train)
self.rec4.fit(topK=30, alpha=0.21, beta=0.25)
target_users = data.urm_train_users_by_type[2][1]
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(data, recommenders=[self.rec1, self.rec2, self.rec3],
recommended_users=target_users, max_cutoff=30)
def __init__(self, data: DataObject):
super(Hybrid100AlphaRecommender, self).__init__(data.urm_train)
self.data = data
self.rec1 = UserKNNCBFRecommender(data.ucm_all, data.urm_train)
self.rec2 = TopPop(data.urm_train)
self.rec1.fit(topK=5000,
shrink=5,
feature_weighting="TF-IDF",
similarity="euclidean")
self.rec2.fit()
cold = data.ids_cold_user
train_cold = data.urm_train_users_by_type[0][1]
if train_cold.shape[0] > 0:
target_users = np.append(cold, train_cold)
else:
target_users = cold
self.target_users = target_users
self.hybrid_rec = Hybrid1CXAlphaRecommender(
data,
recommenders=[self.rec1, self.rec2],
recommended_users=target_users,
max_cutoff=20)
feature_weighting="TF-IDF",
similarity="cosine")
base_recommenders.append([rec1, rec2, rec3, rec4, rec5])
# TODO: Edit here
# Insert the type of users to test the recommenders
n, t_users, description = datas[j].urm_train_users_by_type[1]
tested_users.append(t_users)
# TODO: Edit here
# This code auto assign the weights
# It is possible to manually assign the weights
# For example, we can assign the weights of a previously tuned recommender
cached_hybrid_rec = Hybrid1CXAlphaRecommender(datas[j],
base_recommenders[j],
tested_users[j],
max_cutoff=max_cutoff)
# Edit here the weight if necessary
# The size of the weights matrix must be bigger of the cutoff
cached_hybrid_rec.weights = np.array([
[
1.0, 0.7803233193098372, 0.4042650517804784,
0.26444366709538053, 0.2752283977870088, 0.23751321242187357,
0.24058696112550104, 0.23590434442820032, 0.1992990685621503,
0.1878790301115923, 0.16690930495160153, 0.15325757938895435,
0.1655181857400707, 0.16904924923310471, 0.13097329147255055,
0.12288596504305309, 0.11857054225592471, 0.1219683401906944,
0.12310761424074275, 0.12706253865684247, 0.10616907384825704,
0.11435056385016795, 0.10762546479680782, 0.10853202794520854,
0.10581279995920735, 0.030542092731989226, 0.02815866621379197,
0.029606210545982038, 0.030081711307335356,