def __init__(self, urm, urm_t, icm, icm2, enable_dict, urm_test):
self.urm = urm
self.n_users, self.n_items = urm.getCSR().shape
self.setEnables(enable_dict )
self.item_item = IIHybridRecommender(urm, icm, icm2)
self.item_item.fit(item_weight=0.4, cbf1_weight=0.25, cbf2_weight=0.1)
self.user = CollaborativeFiltering()
self.user.fit(urm_t, k=100, h=0, mode='user')
if self.enableSVD:
self.svd = SVDRecommender(urm, nf=385)
if self.enableP3A:
self.p3a = P3alpha(urm.getCSR())
self.p3a.fit(topK=80, alpha=1, min_rating=0, implicit=True, normalize_similarity=True)
if self.enableSLIM:
choice = 2
logFile = open("SLIM_BPR_Cython.txt", "a")
self.slim = SLIM_BPR_Cython(urm.getCSR(), recompile_cython=False, positive_threshold=0,
URM_validation=urm_test.getCSR(), final_model_sparse_weights=True,
train_with_sparse_weights=False)
self.slim.fit(epochs=100, validation_every_n=1, logFile=logFile, batch_size=5, topK=200,
sgd_mode="adagrad", learning_rate=0.075)
if self.enableLFM:
# LightFM
print("starting USER CF")
self.lfm = LightFMRecommender()
self.lfm.fit(urm, epochs=100)
print("USER CF finished")
def __init__(self, urm, urm_t, icm, icm2, enable_dict, urm_test=None):
self.urm = urm
self.setEnables(enable_dict)
if self.enableSVD:
self.svd = SVDRecommender(urm, nf=385)
if self.enableSLIM:
logFile = open("SLIM_BPR_Cython.txt", "a")
self.slim = SLIM_BPR_Cython(urm.getCSR(),
recompile_cython=False,
positive_threshold=0,
URM_validation=urm_test.getCSR(),
final_model_sparse_weights=True,
train_with_sparse_weights=False)
self.slim.fit(epochs=100,
validation_every_n=1,
logFile=logFile,
batch_size=5,
topK=200,
sgd_mode="adagrad",
learning_rate=0.075)
self.slim_sim = self.slim.get_similarity()
if self.enableLFM:
# LightFM
print("starting USER CF")
self.lfm = LightFMRecommender()
self.lfm.fit(urm, epochs=100)
print("USER CF finished")
# User based
print("starting USER CF")
self.cbu = CollaborativeFiltering()
self.cbu.fit(urm_t, k=100, h=8, mode='user')
print("USER CF finished")
# Item based
print("starting ITEM CF")
self.cbi = CollaborativeFiltering()
self.cbi.fit(urm, k=125, h=10, mode='item')
print("ITEM CF finished")
# Content based artist
print("starting CBF")
self.cbf = ContentBasedFiltering(icm, urm, k=25, shrinkage=100)
self.cbf.fit()
print("CBF finished")
if self.enableCBF2:
print("starting CBF2")
self.cbf2 = ContentBasedFiltering(icm2, urm, k=25, shrinkage=100)
self.cbf2.fit()
print("CBF2 finished")
def __init__(self, urm, urm_t, icm, icm2, enable_dict, urm_test=None):
self.urm = urm
self.setEnables(enable_dict)
if self.enableRP3B:
self.rp3b = RP3betaRecommender(urm.getCSR())
self.rp3b.fit(topK=100,
alpha=0.7,
beta=0.3,
normalize_similarity=True,
implicit=True)
if self.enableSLIM:
choice = 2
logFile = open("SLIM_BPR_Cython.txt", "a")
self.slim = SLIM_BPR_Cython(urm.getCSR(),
recompile_cython=False,
positive_threshold=0,
URM_validation=urm_test.getCSR(),
final_model_sparse_weights=True,
train_with_sparse_weights=False)
self.slim.fit(epochs=100,
validation_every_n=1,
logFile=logFile,
batch_size=5,
topK=200,
sgd_mode="adagrad",
learning_rate=0.075)
self.slim_sim = self.slim.get_similarity()
if self.enableP3A:
self.p3a = P3alpha(urm.getCSR())
self.p3a.fit(topK=80,
alpha=1,
min_rating=0,
implicit=True,
normalize_similarity=True)
# if self.enableCBF2:
# print("starting CBF2")
# self.cbf2 = ContentBasedFiltering(icm2, urm, k=25, shrinkage=0)
# self.cbf2.fit()
# print("CBF2 finished")
if self.enableLFM:
# LightFM
print("starting USER CF")
self.lfm = LightFMRecommender()
self.lfm.fit(urm, epochs=100)
print("USER CF finished")
if self.enableSVD:
self.svd = PureSVDRecommender(urm.getCSR())
self.svd.fit(num_factors=225)
print("USER CF finished")
# User based
print("starting USER CF")
self.cbu = CollaborativeFiltering()
self.cbu.fit(urm_t, k=100, h=0, mode='user')
print("USER CF finished")
self.item_item = IIHybridRecommender(urm, icm, icm2)
self.item_item.fit(item_weight=0.4, cbf1_weight=0.25, cbf2_weight=0.1)
rp3b = RP3betaRecommender(urm.getCSR())
rp3b.fit(**cf_parameters)
if submission:
recommended_items = rp3b.m_recommend(targetList, nRec=10)
generate_output(targetList, recommended_items)
else:
cumulative_precision, cumulative_recall, cumulative_MAP = evaluate_algorithm(
urm_test, rp3b)
print(
"Recommender, performance is: Precision = {:.4f}, Recall = {:.4f}, MAP = {:.6f}"
.format(cumulative_precision, cumulative_recall, cumulative_MAP))
elif htype == "lfm":
lfm = LightFMRecommender()
lfm.fit(urm.getCSR(), epochs=100)
if submission:
recommended_items = lfm.m_recommend(targetList, nRec=10)
generate_output(targetList, recommended_items)
else:
cumulative_precision, cumulative_recall, cumulative_MAP = evaluate_algorithm(
urm_test, lfm)
print(
"Recommender, performance is: Precision = {:.4f}, Recall = {:.4f}, MAP = {:.6f}"
.format(cumulative_precision, cumulative_recall, cumulative_MAP))
elif htype == "xgb":
enable_dict = {
'enableSVD': False,